Jurnal Ilmiah Informatika Komputer
type: channel
description: Jurnal Ilmiah Informatika Komputer diterbitkan secara berkala tiga kali dalam setahun, yaitu pada bulan April, Agustus, dan Desember. Jurnal ini memuat artikel ilmiah hasil penelitian di bidang Teknologi Informasi dan Komunikasi, yang ditulis dalam bahasa Indonesia maupun bahasa Inggris. Jurnal ini diterbitkan oleh Bagian Publikasi Universitas Gunadarma dan telah terakreditasi Peringkat 3 (SINTA 3) oleh RISTEKDIKTI. Jurnal Ilmiah Informatika Komputer is published periodically three times a year in April, August, and December. The journal publishes a broad range of research articles in the field of Information Technology and Communication, written in either Indonesian or English. This journal is published by the Publication Division of Universitas Gunadarma and is nationally accredited by DIKTI (Decree No. 177/E/KPT/2024) with a SINTA 3 ranking.
publisher: Universitas Gunadarma
language: en
publication name: Jurnal Ilmiah Informatika Komputer
issn: 0853-8638
items: Rancang Bangun Sistem Pengering Sepatu Dengan Menggunakan Aplikasi Telegram Berbasis ESP32
items: Development and Preliminary Validation of PQC-ORI: A Public-Sector Readiness Assessment Instrument for Post-Quantum Cryptography
items: Klasifikasi Jenis Daging Menggunakan Algoritma YOLOv8
items: Purwarupa Alat Pemilah Warna Barang Menggunakan Sensor TCS230
items: Penerapan Deepface dan Retinaface dalam Pengenalan Wajah Parsial Untuk Aplikasi Keamanan Digital
items: Implementasi K-Means dan Elbow dalam Sistem Informasi Kepegawaian untuk Pengelompokan Data Karyawan PTPN Medan
items: Pengembangan Aplikasi Website untuk Klasifikasi Status Gizi Balita Menggunakan Integrasi SMOTE pada Algoritma Naïve Bayes
items: Analisis Faktor- Faktor Penentu Efektifitas Pemanfaatan Integrasi Meta AI Dalam Sosial Media Whatsapp: Pendekatan Expectation Confirmation
items: Apa yang Mendorong Pengguna Untuk Mengadopsi Centralized Exchange (CEX)? Tinjauan Literatur Sistematis Menggunakan Metode PRISMA
items: A Secure Geotagging Authentication Mechanism Using EXIF Metadata for Digital Public Works Complaint Platforms
items: Smart Agriculture Prototype : An IoT - Controlled System for Lightning and Nutrient Management in Dragon Fruit Farming
items: Sistem Pembelajaran Adaptif Berbasis Web Lonewolf Academy untuk Meningkatkan Keterlibatan dan Personalisasi Pembelajaran
items: Segmentasi Konsumen E-Commerce Menggunakan Algoritma K-Means Berbasis Dashboard Interaktif
items: Pengembangan Aplikasi Berbasis Web untuk Skrining Tingkat Depresi Ibu Pasca Melahirkan Menggunakan Skala EPDS dan PHQ-9
items: Rancang Bangun Aplikasi Walking Log dalam Penghitungan Jumlah Langkah untuk Pembakaran Kalori Tubuh
items: Implementasi Algoritma AES-256 Berbasis Web untuk Pengamanan Dokumen Digital di CV. Karya Eksklusif Nusantara
items: Identifikasi Tingkat Kepatuhan Wajib Pajak Bumi dan Bangunan di Jakarta dengan Pendekatan Algoritma K-Means
items: Implementasi Data Mining Naïve Bayes untuk Sistem Peringatan Dini Hasil Belajar Siswa Sekolah Dasar
items: BISINDO Sign Letters Recognition Through HOG Features and Bagging Decision Tree
items: Implementasi Metode CNN Berbasis Transfer Learning dengan Arsitektur MobileNetV2 dalam Klasifikasi dan Pemetaan Tempat Wisata

Rancang Bangun Sistem Pengering Sepatu Dengan Menggunakan Aplikasi Telegram Berbasis ESP32
type: item
description: Sepatu merupakan salah satu alas kaki yang banyak digunakan untuk beraktivitas diluar ruangan. Ketika musim hujan tiba, terkadang sepatu menjadi basah dan tidak dapat digunakan kembali di hari berikutnya. Kondisi sepatu yang basah juga dapat disebabkan oleh aktivitas luar ruangan. Sepatu yang basah dapat menyebabkan bau tidak sedap, pertumbuhan jamur, serta kerusakan material sepatu. Proses pengeringan secara konvensional sering kali kurang efektif karena bergantung pada cuaca dan memerlukan waktu yang lama. Penelitian ini bertujuan untuk merancang dan membuat alat pengering sepatu otomatis dengan menggunakan aplikasi telegram untuk mempercepat proses pengeringan dan pemantauan kondisi sepatu secara real-time. Pada penelitian ini, jenis sepatu yang digunakan adalah sepatu berbahan kanvas, synthetic leather dan mesh untuk sepatu dewasa dan sistem akan mati secara otomatis berdasarkan timer yang sudah ditentukan sebelumnya dan disesuaikan dengan nilai kelembabannya atau dari aplikasi Telegram. Metode yang digunakan meliputi perancangan sistem berbasis mikrokontroler ESP32 yang terhubung dengan sensor DHT11 untuk mengukur suhu dan kelembaban, serta modul relay untuk mengendalikan kipas, lampu pijar, dan perangkat shoe dryer. Sistem ini dilengkapi dengan tampilan LCD dan terhubung dengan aplikasi Telegram untuk kontrol jarak jauh serta notifikasi otomatis. Hasil pengujian menunjukkan bahwa bahan sepatu seperti synthetic leather dan mesh dapat mengering sempurna dengan nilai kelembaban adalah 0% dalam waktu 3 jam, sedangkan sepatu dengan bahan kanvas membutuhkan waktu lebih lama yaitu 3 jam 23 menit dengan kelembaban sekitar 7–9%. Sistem ini dapat mengeringkan sepatu terutama bagian dalamnya dengan sempurna.
subject: Articles
  topic: section
subject: Sepatu Bahan Sintetis
  topic: keywords
subject: Sepatu Bahan Kanvas
  topic: keywords
subject: Pengering Sepatu
  topic: keywords
subject: ESP32
  topic: keywords
subject: Telegram Bot
  topic: keywords
creator: Bintang Amalia Putri
creator: Emy Haryatmi
creator: Tri Agus Riyadi
creator: Ary Bima Kurniawan
rights: Copyright (c) 2026 Jurnal Ilmiah Informatika Komputer https://creativecommons.org/licenses/by/4.0
date: 2026-07-27
publication date: 2026-07-27
starting page: 1
ending page: 13
doi: 10.35760/ik.2026.v31i1.632

Development and Preliminary Validation of PQC-ORI: A Public-Sector Readiness Assessment Instrument for Post-Quantum Cryptography
type: item
description: The emergence of quantum computing technologies poses significant risks to conventional public-key cryptographic systems and creating an urgent need for organizational preparation toward Post-Quantum Cryptography (PQC) migration. Although various PQC migration frameworks and quantum-readiness initiatives have been proposed, existing approaches primarily focus on technical migration guidance and provide limited operational mechanisms for assessing organizational readiness, particularly within public-sector environments. This study develops and conducts a preliminary validation of a Post-Quantum Cryptography Organizational Readiness Instrument (PQC-ORI) for Indonesian public-sector organizations using a Design Science Research approach integrating Systematic Literature Review (SLR), Qualitative Content Analysis (QCA), and Framework Alignment Matrix (FAM) techniques to synthesize recurring organizational readiness constructs from PQC migration literature, governance frameworks, and cybersecurity readiness studies. The resulting framework comprises six organizational readiness dimensions: Cryptographic Awareness, Governance and Policy, Migration Management, Technical Capability, Cryptographic Agility, and Operational Resilience. The framework additionally operationalizes these dimensions into structured assessment indicators and a five-level maturity model intended to support organizational self-assessment and migration planning activities. Preliminary validation by three domain experts using structured expert judgment yielded an overall mean score of 3.69/4.00, indicating high relevance, conceptual consistency, and applicability for assessing organizational readiness for PQC migration in public-sector organizations.
subject: Articles
  topic: section
subject: Post-Quantum Cryptography
  topic: keywords
subject: Organizational Readiness Framework
  topic: keywords
subject: Readiness Assessment Instrument
  topic: keywords
subject: Design Science Research
  topic: keywords
subject: Public Sector
  topic: keywords
creator: Fadlilah Izzatus Sabila
creator: Fauziah
rights: Copyright (c) 2026 Jurnal Ilmiah Informatika Komputer https://creativecommons.org/licenses/by/4.0
date: 2026-07-31
publication date: 2026-07-31
starting page: 14
ending page: 30
doi: 10.35760/ik.2026.v31i1.325

Klasifikasi Jenis Daging Menggunakan Algoritma YOLOv8
type: item
description: Dalam kehidupan sehari-hari tubuh manusia memerlukan konsumsi daging sapi sebagai salah satu sumber protein hewani karena memiliki kandungan zat besi, selenium, zinc, vitamin B kompleks dan omega 3. Dalam melakukan pembelian daging sapi merupakan persoalan tersendiri bagi masyarakat awam, karena secara kasat mata bentuk daging sapi dan daging lainnya, terutama daging babi, sangat tidak mudah untuk dibedakan. Kesulitan keterbatasan visual manusia yang timbul tersebut menyebabkan konsumen seringkali tertipu saat membeli daging sapi. Perbedaan secara umum kedua daging tersebut terletak pada warna dan tekstur daging. Untuk mengatasi hal tersebut diperlukan adanya peran teknologi yang bisa digunakan untuk membantu membedakan pengenalan jenis daging agar konsumen dapat mengenalinya secara lebih akurat. Penerapan model Deep Learning dengan menggunakan algoritma Convolutional Neural Network yaitu You Only Look Once v8 (YOLOv8) menjadi salah satu metode yang dapat diterapkan untuk mengenali daging sapi pada bidang informatika. Precision 0.974, Recall 1, mAP 0.955 menunjukkan hasil penelitian kinerja dan waktu komputasi menggunakan YOLOv8 pada daging babi, sedangkan pada daging sapi metode YOLOv8 menghasilkan Precision 1, Recall 0.994, mAP 0.995 dengan waktu komputasi kurang lebih 56.52 menit.
subject: Articles
  topic: section
subject: Daging
  topic: keywords
subject: Machine learning
  topic: keywords
subject: YOU ONLY LOOK ONCE v8 (YOLOv8)
  topic: keywords
creator: Dyah Cita Irawati
creator: Nur Fitriansyah Aji
rights: Copyright (c) 2025 Jurnal Ilmiah Informatika Komputer https://creativecommons.org/licenses/by/4.0
date: 2025-08-31
publication date: 2025-08-31
starting page: 199
ending page: 206
doi: 10.35760/ik.2025.v30i2.243

Purwarupa Alat Pemilah Warna Barang Menggunakan Sensor TCS230
type: item
description: Technological developments have driven automation in various industrial sectors, including color-based sorting systems. Currently, many industries still rely on human labor in the sorting process, which results in high operational costs and is prone to human error. This study aims to design a prototype of an object sorting device based on color using the TCS230 sensor integrated with an Arduino Uno and controlled by a PLC (Programmable Logic Controller) as the main unit. The system uses push buttons, TCS230 sensors, and proximity sensors as inputs, with the Arduino functioning as the initial signal processor. The PLC processes data sequentially to control the outputs, which include a DC motor as the conveyor drive, two pneumatic solenoids for item separation, and status indicator lights. Test results show that the TCS230 sensor consistently reads red (14.72 kHz), green (6.944 kHz), and blue (20.82 kHz) colors. The PLC responds according to logic as red activates solenoid 1, green activates solenoid 2, while blue does not trigger the actuator. The system has proven to be effective, accurate, and reliable, demonstrating significant potential for application in manufacturing, logistics, and recycling industries.
subject: Articles
  topic: section
subject: Alat Pemilah Barang
  topic: keywords
subject: Warna
  topic: keywords
subject: Sensor TCS230
  topic: keywords
subject: PLC (Programmable Logic Controller)
  topic: keywords
creator: Wahyu Kusuma Raharja
creator: R.A. Sekar Ciptaning Anindya
creator: M. Anggita Sari
creator: J.F. Saputra
rights: Copyright (c) 2025 Jurnal Ilmiah Informatika Komputer https://creativecommons.org/licenses/by/4.0
date: 2025-08-31
publication date: 2025-08-31
starting page: 188
ending page: 198
doi: 10.35760/ik.2025.v30i2.242

Penerapan Deepface dan Retinaface dalam Pengenalan Wajah Parsial Untuk Aplikasi Keamanan Digital
type: item
description: Pengenalan wajah merupakan aspek penting dalam teknologi keamanan modern dan memiliki banyak aplikasi. Namun demikian teknologi ini juga menghadapi tantangan, terutama dalam mengidentifikasi wajah yang hanya sebagian terlihat akibat berbagai kondisi seperti tertutup masker, kacamata atau benda lain. Penelitian ini mengusulkan solusi penegnalan wajah parsial berbasis deep learning dengan mengintegrasikan RetinaFace sebagai detektor wajah dan ArcFace yang diimplementasikan melalui framework DeepFace sebagai ekstraktor fitur untuk meningkatkan akurasi identifikasi wajah tidak utuh. Dataset dikumpulkan menggunakan kamera ponsel dengan variasi pencahayaan, ekspresi, dan oklusi (masker/kacamata), kemudian diproses melalui pipeline prapengolahan citra yang mencakup konversi warna, resizing, dan normalisasi. Model dilatih dengan loss function berbasis angular margin untuk memaksimalkan jarak antar kelas, dioptimasi menggunakan Adam (learning rate 0.001) dan dievaluasi melalui confusion matrix. Hasil pengujian menunjukkan akurasi 92% pada citra statis dan 94% dalam skenario realtime, dengan kesalahan prediksi terutama terjadi pada wajah tertutup lebih dari 50%. Keunggulan sistem ini terletak pada kombinasi deteksi multi-tugas RetinaFace (landmark + bounding box) dan ekstraksi fitur diskriminatif ArcFace, yang terbukti robust terhadap variasi parsial. Temuan ini mendukung aplikasi praktis seperti absensi digital dan keamanan berbasis kamera, dengan rekomendasi peningkatan kualitas dataset dan hardware untuk mengurangi false negative.
subject: Articles
  topic: section
subject: Pengenalan wajah parsial
  topic: keywords
subject: RetinaFace
  topic: keywords
subject: ArcFace
  topic: keywords
subject: deep learning
  topic: keywords
subject: biometrik
  topic: keywords
creator: Helmi Yulianti Fauziah
creator: Tubagus Maulana Kusuma
rights: Copyright (c) 2025 Jurnal Ilmiah Informatika Komputer https://creativecommons.org/licenses/by/4.0
date: 2025-08-31
publication date: 2025-08-31
starting page: 175
ending page: 187
doi: 10.35760/ik.2025.v30i2.241

Implementasi K-Means dan Elbow dalam Sistem Informasi Kepegawaian untuk Pengelompokan Data Karyawan PTPN Medan
type: item
description: Pengelolaan data kepegawaian secara manual di unit kebun PTPN IV Regional I Medan sering mengalami kendala dalam hal efisiensi dan objektivitas evaluasi kinerja karyawan. Penelitian ini bertujuan untuk mengembangkan sistem informasi kepegawaian berbasis web yang mampu mengelompokkan karyawan secara otomatis menggunakan algoritma K-Means Clustering dengan jumlah klaster optimal yang ditentukan oleh Metode Elbow. Penelitian menggunakan metode kuantitatif dengan data primer berupa atribut karyawan seperti absensi, masa kerja, usia, pendidikan, kinerja, dan divisi. Sistem dirancang menggunakan framework Laravel dan diuji menggunakan metode Black Box Testing. Hasil penelitian menunjukkan bahwa integrasi K-Means dan Elbow dapat menghasilkan pengelompokan karyawan ke dalam tiga kategori kinerja (tinggi, sedang, rendah) secara akurat dan efisien. Sistem ini dapat digunakan oleh HRD sebagai dasar dalam pengambilan keputusan manajerial secara objektif.
subject: Articles
  topic: section
subject: Data Mining
  topic: keywords
subject: Elbow Method
  topic: keywords
subject: K-Means Clustering
  topic: keywords
subject: Laravel
  topic: keywords
subject: Sistem Informasi Kepegawaian
  topic: keywords
creator: Windi Aninda Fitrah
creator: Aninda Muliani Harahap
rights: Copyright (c) 2025 Jurnal Ilmiah Informatika Komputer https://creativecommons.org/licenses/by/4.0
date: 2025-08-27
publication date: 2025-08-27
starting page: 164
ending page: 174
doi: 10.35760/ik.2025.v30i2.14905

Pengembangan Aplikasi Website untuk Klasifikasi Status Gizi Balita Menggunakan Integrasi SMOTE pada Algoritma Naïve Bayes
type: item
description: Status gizi balita menjadi indikator krusial dalam menilai kondisi kesehatan dan pertumbuhan anak, sehingga penelitian ini bertujuan mengembangkan sistem klasifikasi status gizi balita berbasis machine learning dengan menggunakan data dari Puskesmas Rajeg sebanyak 6062 entri. Proses diawali dengan pemrosesan dataset dalam format .csv, dilanjutkan dengan pembersihan data, pemilihan atribut penting, penanganan data hilang, penghapusan outlier menggunakan metode interquartile range (IQR), dan penghilangan duplikasi. Selanjutnya, data diolah melalui label encoding, normalisasi MinMaxScaler, dan dibagi menjadi data latih dan data uji dengan rasio 70:30. Untuk menangani ketidakseimbangan kelas pada atribut target (BB/TB), digunakan metode SMOTE agar distribusi kelas menjadi seimbang. Model klasifikasi yang digunakan adalah Naïve Bayes dan dievaluasi menggunakan metrik akurasi, presisi, recall, serta F1-score. Hasil menunjukkan bahwa model tanpa SMOTE memiliki akurasi lebih tinggi (89%) dibandingkan model dengan SMOTE (63%), meskipun model dengan SMOTE lebih baik dalam mengenali kelas minoritas. Sistem ini diimplementasikan dalam bentuk aplikasi website prediksi status gizi yang interaktif dan mudah digunakan, sehingga memudahkan tenaga kesehatan dalam menginput data, mencari informasi, serta memantau status gizi balita secara efisien.
subject: Articles
  topic: section
subject: Machine Learning
  topic: keywords
subject: Naive Bayes
  topic: keywords
subject: SMOTE
  topic: keywords
subject: Gizi Balita
  topic: keywords
subject: Klasifikasi
  topic: keywords
creator: Damar Kuncoro Adji
creator: Taufik Hidayat
creator: Djamaludin Djamaludin
rights: Copyright (c) 2025 Jurnal Ilmiah Informatika Komputer https://creativecommons.org/licenses/by/4.0
date: 2025-08-27
publication date: 2025-08-27
starting page: 154
ending page: 163
doi: 10.35760/ik.2025.v30i2.14845

Analisis Faktor- Faktor Penentu Efektifitas Pemanfaatan Integrasi Meta AI Dalam Sosial Media Whatsapp: Pendekatan Expectation Confirmation
type: item
description: Penelitian ini mengkaji faktor-faktor yang berkontribusi terhadap integrasi yang efektif dari teknologi Generative Artificial Intelligence (GenAI) ke dalam penyampaian pesan di media sosial. Meskipun pemanfaatan AI dalam komunikasi digital semakin berkembang, dan banyak digunakan dalam komunikasi digital, kajian yang membahas persepsi pengguna dan faktor-faktor yang mendukung integrasi yang efektif ke dalam pesan media sosial, khususnya melalui pendekatan Konfirmasi Harapan, masih jarang ditemukan. Penelitian ini bertujuan mengidentifikasi faktor-faktor kunci yang mempengaruhi keberlanjutan penggunaan GenAI dalam strategi komunikasi media sosial Meta (Instagram, WhatsApp, Messenger) dan menguji hubungan antar variabel Expectation Confirmation Theory (ECT) yang diperluas. Penggunaan pendekatan kuantitatif berbasis ECT, data dikumpulkan melalui survei daring terhadap 131 responden dan dianalisis menggunakan Partial Least Squares Structural Equation Modeling (PLS-SEM). Hasil menunjukkan bahwa Continuance intention memiliki koefisien determinasi tertinggi (R² = 0,464). Perceived Usefulness (R² = 0,41; p < 0,001) dan Satisfaction (R² = 0,38; p < 0,001) berpengaruh langsung dan signifikan terhadap continuance intention, diikuti Perceived Enjoyment (R² = 0,33; p < 0,01) dan Perceived Ease of Use (R² = 0,27; p < 0,05). Temuan ini menekankan pentingnya kesesuaian antara kinerja GenAI dan harapan pengguna dalam menciptakan pengalaman komunikasi digital yang bernilai. Studi ini memberikan wawasan strategis bagi praktisi komunikasi dan pengembang AI untuk memperkuat adopsi teknologi GenAI di ranah media sosial.
subject: Articles
  topic: section
subject: Integrasi Teknologi
  topic: keywords
subject: Kecerdasan Buatan Generatif
  topic: keywords
subject: Media Sosial
  topic: keywords
subject: Persepsi Pengguna
  topic: keywords
subject: Teori Konfirmasi Ekspektasi
  topic: keywords
creator: Timoti James Kaway
creator: Ratna Juita
creator: Dedi I. Inan
rights: Copyright (c) 2025 Jurnal Ilmiah Informatika Komputer https://creativecommons.org/licenses/by/4.0
date: 2025-08-27
publication date: 2025-08-27
starting page: 140
ending page: 153
doi: 10.35760/ik.2025.v30i2.14558

Apa yang Mendorong Pengguna Untuk Mengadopsi Centralized Exchange (CEX)? Tinjauan Literatur Sistematis Menggunakan Metode PRISMA
type: item
description: Centralized Exchanges (CEX) are intermediaries managed by centralized entities, offering a secure and user-friendly platform for cryptocurrency trading. Despite the rapid adoption of CEXs, particularly in Indonesia a systematic understanding of the factors influencing users’ intention to use these platforms remains limited. This study conducts a Systematic Literature Review (SLR) to identify, classify, and analyze the behavioural intention factors toward CEX usage from 2015 to 2025. Utilizing the PRISMA 2020 framework, the review integrates articles sourced from Scopus, PubMed, and Web of Science using a validated combination of keywords. A total of 27 relevant studies were identified after applying inclusion and exclusion criteria. The analysis reveals a growing body of research interest starting from 2018 and keep growing until now, with a concentration of studies in Asia, particularly in China and India. Most studies apply established models such as Technology Acceptance Model (TAM), Unified Theory of Acceptance and Use of Technology (UTAUT), and Theory of Planned Behaviour (TPB). This systematic review identified that the predominant factors explored across studies are risk perception, trust, awareness, ease of use, and users' attitudes. However, research gaps remain in areas such as emotional experience, social influence, and regulatory context across various geographical regions. This literature review provides a comprehensive mapping of current research distributions and proposes directions for future exploration in the domain of centralized cryptocurrency exchanges
subject: Articles
  topic: section
subject: Behavioral Intention
  topic: keywords
subject: Intention to Use
  topic: keywords
subject: Centralized Exchange
  topic: keywords
subject: Cryptocurrency Adoption
  topic: keywords
subject: Systematic Literature Review
  topic: keywords
creator: Liauwnad Steven
creator: Lisana Lisana
rights: Copyright (c) 2025 Jurnal Ilmiah Informatika Komputer https://creativecommons.org/licenses/by/4.0
date: 2025-08-27
publication date: 2025-08-27
starting page: 130
ending page: 139
doi: 10.35760/ik.2025.v30i2.14639

A Secure Geotagging Authentication Mechanism Using EXIF Metadata for Digital Public Works Complaint Platforms
type: item
description: Sistem pengaduan publik di lembaga pemerintah daerah sering menghadapi tantangan seperti laporan yang tidak valid, proses verifikasi manual yang lambat, dan ketiadaan integrasi lokasi pelapor. Untuk mengatasi masalah tersebut, studi ini mengembangkan sistem informasi pengaduan publik berbasis web untuk Kantor PUPR Kota Baubau dengan menerapkan mekanisme validasi otomatis menggunakan metadata EXIF yang tertanam dalam foto pengaduan. Sistem ini dikembangkan menggunakan pendekatan Waterfall yang memungkinkan proses perancangan berjalan terstruktur dan terdokumentasi dengan baik. Fitur utamanya mencakup validasi otomatis lokasi dan waktu pengambilan foto, penolakan laporan tidak valid, deteksi format dan ukuran file, serta antarmuka responsif tanpa kebutuhan login. Setiap pengaduan yang berhasil dikirimkan juga akan otomatis masuk ke email petugas terkait untuk mempercepat tindak lanjut. Hasil pengujian fungsional menggunakan metode black box menunjukkan seluruh fitur berjalan sesuai spesifikasi, sementara uji beta menghasilkan skor rata-rata 3,43 dan indeks kepuasan sebesar 85,75% yang tergolong Sangat Baik. Temuan ini menunjukkan bahwa sistem mampu menyaring laporan tidak relevan, mempercepat verifikasi, meningkatkan keakuratan data, serta mendukung akses publik melalui perangkat seluler dan desktop.
subject: Articles
  topic: section
subject: EXIF Metadata
  topic: keywords
subject: Geotagging
  topic: keywords
subject: GIS
  topic: keywords
subject: Validasi Lokasi Otomatis
  topic: keywords
subject: Pengaduan Masyarakat
  topic: keywords
subject: Sistem Berbasis Web
  topic: keywords
creator: Yasyfa Xena Arleyda Wahyudi
creator: Lilis Nur Hayati
creator: Syahrul Mubarak
rights: Copyright (c) 2025 Jurnal Ilmiah Informatika Komputer https://creativecommons.org/licenses/by/4.0
date: 2025-08-27
publication date: 2025-08-27
starting page: 119
ending page: 129
doi: 10.35760/ik.2025.v30i2.14667

Smart Agriculture Prototype : An IoT - Controlled System for Lightning and Nutrient Management in Dragon Fruit Farming
type: item
description: Perkembangan teknologi mendorong sektor pertanian untuk beradaptasi menuju sistem yang lebih cerdas, efisien, dan terintegrasi. Buah naga salah satu komoditas bernilai ekonomi tinggi, membutuhkan pengelolaan intensitas cahaya, kelembapan dan pemberian nutrisi secara teratur agar dapat tumbuh optimal. Penelitian ini merancang prototipe sistem otomatis berbasis Internet of Things (IoT) yang berfungsi mengendalikan penerangan dan pemberian nutrisi. Sistem dikembangkan menggunakan mikrokontroler ESP32-S3 yang terhubung dengan sensor BH1750 untuk pengukuran intensitas cahaya serta modul RTC DS3231 untuk penjadwalan pemberian nutrisi. Seluruh komponen dikendalikan melalui relay dan dapat dipantau secara real-time menggunakan Telegram Bot. Hasil pengujian menunjukkan bahwa lampu otomatis menyala pada intensitas cahaya 26,08 lux dan mati pada 265,60 lux, sedangkan pompa nutrisi bekerja otomatis setiap dua hari sekali pada pukul 09.14 selama 5 detik. Data pemantauan dan status perangkat berhasil dikirim secara real-time melalui Telegram, menunjukkan bahwa sistem ini efektif dalam meningkatkan efisiensi dan akurasi perawatan tanaman buah naga.
subject: Articles
  topic: section
subject: Buah Naga
  topic: keywords
subject: Otomatitasi
  topic: keywords
subject: Internet of Things
  topic: keywords
creator: Nana Marliza
creator: Nursabilah Hasim
creator: Deny Rochman Arifatno
rights: Copyright (c) 2025 Jurnal Ilmiah Informatika Komputer https://creativecommons.org/licenses/by/4.0
date: 2025-12-31
publication date: 2025-12-31
starting page: 321
ending page: 330
doi: 10.35760/ik.2025.v30i3.87

Sistem Pembelajaran Adaptif Berbasis Web Lonewolf Academy untuk Meningkatkan Keterlibatan dan Personalisasi Pembelajaran
type: item
description: Transformasi digital di bidang pendidikan mendorong pemanfaatan e-learning sebagai pendukung pembelajaran. Namun, sebagian besar platform masih menerapkan pendekatan pembelajaran yang seragam sehingga belum mengakomodasi perbedaan kemampuan dan kecepatan belajar siswa. Berdasarkan hasil observasi, wawancara, dan kuesioner di salah satu sekolah tingkat SMP, sebanyak 95,5% guru menyatakan bahwa metode pembelajaran yang digunakan saat ini belum mampu mengakomodasi perbedaan karakteristik belajar siswa secara efektif. Penelitian ini bertujuan mengembangkan Sistem Pembelajaran Adaptif Berbasis Web Lonewolf Academy yang menyesuaikan jalur dan rekomendasi pembelajaran berdasarkan hasil evaluasi siswa. Pengembangan sistem menggunakan metode Extreme Programming dengan teknik pengumpulan data berupa observasi, wawancara, kuesioner, dan studi literatur. Sistem dilengkapi fitur pembelajaran siswa, manajemen konten guru, kuis adaptif, modul remedial dan pengayaan, dashboard pemantauan, serta gamifikasi. Pengujian menggunakan black box testing dan System Usability Scale (SUS) menunjukkan tingkat keberhasilan pengujian fungsional sebesar 100% dan skor SUS sebesar 66,74 dengan kategori Good. Hasil ini menunjukkan sistem mampu mendukung pembelajaran yang lebih personal dan interaktif di tingkat SMP.
subject: Articles
  topic: section
subject: Sistem Pembelajaran Adaptif
  topic: keywords
subject: Personalisasi Pembelajaran
  topic: keywords
subject: Extreme Programming
  topic: keywords
subject: Usability Testing
  topic: keywords
creator: Muhamad Jafar Elly
creator: Muhammad Fajri Winanda
creator: Atiqah Meutia Hilda
rights: Copyright (c) 2025 Jurnal Ilmiah Informatika Komputer https://creativecommons.org/licenses/by/4.0
date: 2025-08-31
publication date: 2025-08-31
starting page: 207
ending page: 220
doi: 10.35760/ik.2025.v30i2.187

Segmentasi Konsumen E-Commerce Menggunakan Algoritma K-Means Berbasis Dashboard Interaktif
type: item
description: The rapid expansion of the e-commerce industry in Indonesia has led to a substantial increase in consumer behavioral data. This condition highlights the need for analytical approaches capable of identifying transaction patterns and supporting meaningful customer segmentation. This study aims to segment e-commerce customers using the K-Means clustering method and to present the results through an interactive dashboard. The research process includes data cleaning and standardization, followed by the construction of derived variables such as average basket, spend per month, and paylater share to represent customer transaction behavior. The optimal number of clusters was determined by evaluating several metrics, including the Sum of Squared Errors (SSE) graph, Silhouette Score, Calinski–Harabasz Index, and Davies–Bouldin Index. Although the custest Silhouette Score and Calinski–Harabasz Index were obtained at k = 2, the overall evaluation indicated that k = 3 produced a more balanced and interpretable clustering structure. Consequently, three customer segments were identified: customers with moderate shopping activity, high-value customers with a tendency to use Paylater services, and customers with high transaction frequency but relatively low purchase value. The analytical results were subsequently implemented in a Streamlit-based interactive dashboard to support data-driven decision making.
subject: Articles
  topic: section
subject: Customer Segmentation
  topic: keywords
subject: E-commerce
  topic: keywords
subject: Interactive Dashboard
  topic: keywords
subject: K-Means Clustering
  topic: keywords
creator: Aru Chevy Rasyid
creator: Fauziah
rights: Copyright (c) 2026 Jurnal Ilmiah Informatika Komputer https://creativecommons.org/licenses/by/4.0
date: 2025-12-31
publication date: 2025-12-31
starting page: 308
ending page: 320
doi: 10.35760/ik.2025.v30i3.70

Pengembangan Aplikasi Berbasis Web untuk Skrining Tingkat Depresi Ibu Pasca Melahirkan Menggunakan Skala EPDS dan PHQ-9
type: item
description: Anxiety and depression are mental health issues that are common in mothers during pregnancy and after childbirth. The prevalence of anxiety and depression among pregnant women is 12.6%, and among postpartum women, it is 10.1%. However, mental health screening is still not an integral part of comprehensive antenatal or postnatal care. This research aims to develop a website-based application for screening postpartum maternal depression levels using the EPDS and PHQ-9 scales. The method used involves application development using the PHP programming language and implementing the EPDS and PHQ-9 scales as assessment instruments. The research findings are a screening application that can help postpartum mothers self-assess their level of depression. The website has been successfully implemented and can be accessed online via the link https://postnatalcare.my.id/. The application is expected to serve as a screening tool for determining the level of postpartum depression in mothers and to assist midwives in monitoring patients' mental health conditions, enabling them to provide appropriate care.
subject: Articles
  topic: section
subject: Web-Based Application
  topic: keywords
subject: EPDS
  topic: keywords
subject: Postpartum Mothers
  topic: keywords
subject: PHQ-9
  topic: keywords
subject: Depression Screening
  topic: keywords
creator: Primanita Pangestu
creator: Henny Widowati
creator: Dody Pernadi
rights: Copyright (c) 2026 Jurnal Ilmiah Informatika Komputer https://creativecommons.org/licenses/by/4.0
date: 2025-12-31
publication date: 2025-12-31
starting page: 297
ending page: 307
doi: 10.35760/ik.2025.v30i3.69

Rancang Bangun Aplikasi Walking Log dalam Penghitungan Jumlah Langkah untuk Pembakaran Kalori Tubuh
type: item
description: Walking is a light physical activity that has a positive impact on health. A pedometer can be used to count the number of steps taken while walking. However, these devices usually have limited features and seem impractical to use. Current mobile technology developments, especially smartphones, can be used as an alternative to using a pedometer. The Walking Log application was created as an application to count the number of steps walked and the body's calorie burn. This application was created through several stages including planning, design, coding, and testing. In the planning stage, information will be collected regarding the number of steps, calorie burn, the sensors used, and the process of creating an Android application. At the design stage, a navigation structure, display design and file structure for storing historical data will be created.. In the coding stage, the application will be created using Android Studio. This research produced an Android application file named walking-log.apk with a size of 3,023 kilobytes. The walking log application consists of six menus: the main screen, track menu, settings menu, history menu, and about menu. Based on the results of the trial process, the application can run well on several devices that have met the minimum requirements to be able to run this application, namely Android smartphones with a minimum Android operating system. In the accuracy and error percentage tests, this application has an error percentage of ±2.12%. This error value shows that the application that has been built has good accuracy
subject: Articles
  topic: section
subject: Application
  topic: keywords
subject: Walking Log
  topic: keywords
subject: number of Steps
  topic: keywords
subject: body calories
  topic: keywords
creator: Wahyu Kusuma Raharja
creator: M. Fajar Azhari
creator: Zidan Alfitra
rights: Copyright (c) 2026 Jurnal Ilmiah Informatika Komputer https://creativecommons.org/licenses/by/4.0
date: 2025-12-31
publication date: 2025-12-31
starting page: 287
ending page: 296
doi: 10.35760/ik.2025.v30i3.68

Implementasi Algoritma AES-256 Berbasis Web untuk Pengamanan Dokumen Digital di CV. Karya Eksklusif Nusantara
type: item
description: In the digital era, corporate document security has become a major challenge due to the increasing risk of data breaches and cyberattacks. CV. Karya Eksklusif Nusantara, a company that provides goods and services, still stores important documents in digital formats such as .pdf, .docx, and .xlsx without a special protection system, making them vulnerable to illegal access or data corruption. This study aims to implement the AES-256 algorithm in CBC mode on a web-based system to encrypt and decrypt digital documents, as well as to test its performance based on different file sizes. This research uses the Waterfall method, which includes stages of data collection through interviews and literature review, system requirement analysis, design using UML, implementation with the Laravel framework, and black-box testing. The result of the study is a web-based application capable of securely encrypting and decrypting digital documents. Performance testing shows that the encryption and decryption operations are very fast, with an average encryption time of 0.2924 seconds and a decryption time of 0.3309 seconds, while functional testing ensures that all functions work according to requirements.
subject: Articles
  topic: section
subject: AES-256
  topic: keywords
subject: CBC Mode
  topic: keywords
subject: Document Encryption
  topic: keywords
subject: Web-Based System
  topic: keywords
subject: Data Security
  topic: keywords
creator: Ainun Naqiyah
creator: Lilis Nur Hayati
creator: Amaliah Faradibah
rights: Copyright (c) 2026 Jurnal Ilmiah Informatika Komputer https://creativecommons.org/licenses/by/4.0
date: 2025-12-31
publication date: 2025-12-31
starting page: 275
ending page: 286
doi: 10.35760/ik.2025.v30i3.60

Identifikasi Tingkat Kepatuhan Wajib Pajak Bumi dan Bangunan di Jakarta dengan Pendekatan Algoritma K-Means
type: item
description: The Rural and Urban Land and Building Tax (PBB-P2) serves as a regional fiscal mechanism levied on the ownership, control, or utilization of property assets by both individuals and corporate entities. As a cornerstone of regional fiscal policy, PBB-P2 is instrumental in bolstering Local Own-Source Revenue (PAD). Given Jakarta's status as the nation’s administrative and commercial epicenter characterized by high population density and intense economic momentum, the city holds a strategic and vast potential for PBB-P2 collection. This study aims to categorize the compliance behavior of PBB-P2 taxpayers within the Jakarta region by utilizing the K-Means algorithm. The research methodology is guided by the Cross-Industry Standard Process for Data Mining (CRISP-DM) framework, which involves six systematic phases: business understanding, data understanding, data preparation, modeling, evaluation, and deployment. The dataset consists of 28,125 PBB-P2 taxpayer records collected from 2020 to 2024. The findings reveal that taxpayer compliance is classified into four distinct clusters. Cluster 0 indicates very high compliance, Cluster 1 reflects low compliance, Cluster 2 denotes moderate compliance, and Cluster 3 corresponds to high compliance. An average silhouette coefficient value of 0.742 demonstrates that the resulting clusters are well-defined, showing strong internal similarity and clear separation from one another.
subject: Articles
  topic: section
subject: K-Means Algorithm
  topic: keywords
subject: Clustering
  topic: keywords
subject: Taxpayer Compliance
  topic: keywords
subject: Land and building Tax
  topic: keywords
creator: Muhammad Taufiqul Umam
creator: Baby Lolita Basyah
rights: Copyright (c) 2026 Jurnal Ilmiah Informatika Komputer https://creativecommons.org/licenses/by/4.0
date: 2025-12-31
publication date: 2025-12-31
starting page: 265
ending page: 274
doi: 10.35760/ik.2025.v30i3.59

Implementasi Data Mining Naïve Bayes untuk Sistem Peringatan Dini Hasil Belajar Siswa Sekolah Dasar
type: item
description: This study aims to apply data mining techniques using the Naïve Bayes algorithm to classify student learning outcomes as an early warning system. The classification model was developed using 90 student score records and grouped into two categories, namely “Good” and “Poor”. The experimental results show that the Naïve Bayes model achieved 100% accuracy, precision, and recall. These findings indicate that the proposed model is capable of consistently classifying student learning outcomes and has the potential to serve as a decision support tool for teachers and school administrators.
subject: Articles
  topic: section
subject: Data Mining
  topic: keywords
subject: Classification
  topic: keywords
subject: Naive Bayes
  topic: keywords
subject: Student Learning Outcomes
  topic: keywords
creator: Adyatma Ramadhani S
creator: Atiqah Meutia Hilda
creator: Muhammad Jafar Elly
creator: Nani Mintarsih
rights: Copyright (c) 2025 Jurnal Ilmiah Informatika Komputer https://creativecommons.org/licenses/by/4.0
date: 2025-12-31
publication date: 2025-12-31
starting page: 252
ending page: 264
doi: 10.35760/ik.2025.v30i3.58

BISINDO Sign Letters Recognition Through HOG Features and Bagging Decision Tree
type: item
description: Sign language is one of the primary means of communication for people with hearing disabilities. BISINDO (Indonesian sign language) communicates using hand movements, among other things. One solution to this problem is to use image processing to recognize BISINDO letters A-Z based on hand movements. This study aims to create a BISINDO letter recognition system based on image processing using several stages, namely, preprocessing such as converting RGB images to grayscale images, then improving image quality by adjusting image contrast and removing noise with a median filter, HOG (Histogram of Oriented Gradients) feature extraction, and Bagging Decision Tree classification. A total of 156 images were used in the dataset, consisting of 104 letter images for training data and 52 letter images for test data. The data will be processed in the system as training data, and the dataset will then be stored in ‘mat’ format. Based on the results of testing Classification using Bagging Decision Tree, which produced an average accuracy rate of 86.5%. Thus, this research is expected to contribute to the development of BISINDO character recognition technology based on digital image processing.
subject: Articles
  topic: section
subject: BISINDO
  topic: keywords
subject: Grayscale
  topic: keywords
subject: Median Filter
  topic: keywords
subject: HOG
  topic: keywords
subject: Bagging Decision Tree
  topic: keywords
creator: Dolly Indra
creator: Erick Irawadi Alwi
creator: Faudiah Anwar
creator: St. Nadya Kurnia Prihandani
rights: Copyright (c) 2026 Jurnal Ilmiah Informatika Komputer https://creativecommons.org/licenses/by/4.0
date: 2025-12-31
publication date: 2025-12-31
starting page: 244
ending page: 251
doi: 10.35760/ik.2025.v30i3.57

Implementasi Metode CNN Berbasis Transfer Learning dengan Arsitektur MobileNetV2 dalam Klasifikasi dan Pemetaan Tempat Wisata
type: item
description: The growth of tourism in the digital era encourages the use of social media as a source of visual data for destination analysis. This study aims to classify and map tourist attractions in West Kalimantan using a transfer learning-based Convolutional Neural Network (CNN) method with the MobileNetV2 architecture. A total of 454 images were collected through web scraping from the Instagram account @enjoykalbar, then through a process of elimination, augmentation, normalization, and manual labeling based on the West Kalimantan Disporapar tourism categories, namely Hills, Beaches, Cascades, Culture, Lakes, Rivers, Caves, and Forests. The dataset was divided into training data (70%), validation (20%), and test (10%). The model was built by freezing the initial layers of MobileNetV2 and adding a classification head, then drilled for 20 epochs using the Adam Optimizer and EarlyStopping and ReduceLROnPlateau callbacks. The training results showed a training accuracy of 95.8%, validation accuracy of 88.1%, and test accuracy of 80%. Further evaluation using the classification report yielded an overall accuracy of 89%, with an average precision of 0.93, a recall of 0.86, and an F1-score of 0.88. The model was then integrated into a category- and coordinate-based interactive mapping system to display the distribution of tourist attractions across 12 districts and 2 cities. The results demonstrate that the CNN transfer learning approach is effective for tourism image classification and supports spatial visualization in tourism promotion and planning.
subject: Articles
  topic: section
subject: CNN
  topic: keywords
subject: Transfer Learning
  topic: keywords
subject: MobileNetV2
  topic: keywords
subject: Klasifikasi Citra
  topic: keywords
subject: Pemetaan SIG
  topic: keywords
subject: Tempat Wisata
  topic: keywords
creator: Mira
creator: Christian Cahyaningtyas
creator: Maya Sari
creator: Yuliana
rights: Copyright (c) 2025 Jurnal Ilmiah Informatika Komputer https://creativecommons.org/licenses/by/4.0
date: 2025-12-31
publication date: 2025-12-31
starting page: 233
ending page: 243
doi: 10.35760/ik.2025.v30i3.56