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All Journal STRING (Satuan Tulisan Riset dan Inovasi Teknologi) Technologia: Jurnal Ilmiah Innovation in Research of Informatics (INNOVATICS) Journal Zetroem Jurnal ABDIMAS (Pengabdian kepada Masyarakat) UBJ Jurnal Aplikasi Teknologi Informasi dan Manajemen (JATIM) Journal of Information Technology (JIfoTech) Jurnal Riset dan Aplikasi Mahasiswa Informatika (JRAMI) COVIT Journal of Practical Computer Science (JPCS) JSITP Jurnal Rekayasa Komputasi Terapan Kapas: Kumpulan Artikel Pengabdian Masyarakat Jurnal Rekayasa Sistem Informasi dan Teknologi Jurnal Riset Sistem dan Teknologi Informasi Jurnal Sistem Informasi dan Manajemen Journal of Information Technology, Software Engineering and Computer Science Bulletin of Information System Research Inovasi Gagasan Abdimas & Kuliah Kerja Nyata Elconika: Jurnal Teknik Elektro
Redo Abeputra Sihombing
Universitas Indraprasta PGRI
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Mathematics Medicine & Pharmacology Public Health Social Sciences Other

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Analisis Sentimen Ulasan Aplikasi iPusnas di Playstore dengan Metode Algoritma Random Forest Achmad Fauzi; Andi Dwi Pangestu; Ade Kurnia Solihin; Redo Abeputra Sihombing; Fauzan Natsir
Journal of Information Technology Vol. 6 No. 1 (2026): Journal of Information Technology
Publisher : Institut Shanti Bhuana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46229/jifotech.v6i1.1096

Abstract

iPusnas is a digital library application developed by the National Library of Indonesia (Perpusnas RI) that allows users to borrow and read digital books via smartphones. As one of the most widely used digital library platforms in Indonesia, iPusnas has received thousands of user reviews on Google Play Store, reflecting various public sentiments about the application's performance and features. This study aims to analyze the sentiment of iPusnas user reviews on the Google Play Store using the Random Forest algorithm. Data were collected by scraping user reviews from the Play Store, followed by preprocessing steps including case folding, cleaning, normalization, tokenization, stopword removal, and stemming. Labeling was performed using the Lexicon-Based method. Feature extraction used TF-IDF (Term Frequency-Inverse Document Frequency), and data imbalance was addressed using the SMOTE (Synthetic Minority Over-sampling Technique) method. The results of the analysis showed that the Random Forest model achieved an accuracy of 73,60%, precision of 72,60%, recall of 72,10%, and F1-score of 72,30%, demonstrating its effectiveness in classifying positive and negative sentiments of iPusnas users.
Smart Attendance System: AI Technology for Digital Attendance Using Computer Vision Technology Fauzan Natsir; Redo Abeputra Sihombing; Triana Dewi Salma; Millati Izzatillah; Ega Shela Marsiani; Farhan Maulana Arramsy; Anuj Kumar
ZETROEM Vol 8 No 1 (2026): ZETROEM
Publisher : Prodi Teknik Elektro Universitas PGRI Banyuwangi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36526/ztr.v8i1.7569

Abstract

Employee attendance is a crucial aspect of human resource management, particularly in maintaining discipline and ensuring the operational effectiveness of a company. PT KAMM currently uses a fingerprint-based attendance system which, although effective, often encounters issues such as sensor sensitivity to finger conditions, potential device damage caused by continuous physical contact, and employee inconvenience. This research aims to develop a face recognition-based attendance system as a more efficient and hygienic alternative. The dataset comprises 1,400 facial images from 20 PT KAMM employees (20 classes), split into 80% training, 10% validation, and 10% testing data. The method applied combines the Haar Cascade algorithm for face detection and a Convolutional Neural Network (CNN) for face recognition. The CNN architecture consists of four convolutional layers with 32 to 256 filters, ReLU activation, max pooling, flatten, a 512-neuron fully connected layer, dropout of 0.5, and softmax classification. The model was trained for 50 epochs using the Adam optimizer with a learning rate of 0.001 and batch size of 32. Evaluation was conducted using accuracy, precision, recall, and F1-score metrics. Results show the system achieved an accuracy of 95.71%, precision of 95.80%, recall of 95.60%, and an F1-score of 95.70%, with an average inference time of 0.12 seconds/frame in real-time. However, the system has limitations: accuracy drops by up to 12% under extreme lighting conditions and when employees wear masks. This study is expected to serve as a reference for other companies seeking to adopt similar face recognition technology for contactless attendance management systems.
Analisis Kebutuhan Modul Deteksi Dini Gangguan Mental Lansia sebagai Ekstensi Aplikasi SiLansia dengan Notifikasi untuk Caregiver Redo Abeputra Sihombing; Fauzan Natsir; Triana Dewi Salma; Hoiriyah Hoiriyah; Aisyah Mutia Dawis
Journal of Information Technology, Software Engineering and Computer Science (ITSECS) Vol. 3 No. 4 (2025): Volume 3 Number 4 October 2025
Publisher : PT. Tech Cart Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58602/itsecs.v3i4.362

Abstract

Populasi lanjut usia di Indonesia terus meningkat, dengan proporsi mencapai sekitar 11,75% dari total populasi nasional pada tahun 2023, sejalan dengan meningkatnya risiko gangguan kesehatan mental seperti depresi pada kelompok usia tersebut. Pada penelitian sebelumnya, penulis telah mengembangkan SiLansia, sebuah sistem pemantauan dan rekomendasi kesehatan lansia berbasis Content-Based Filtering (CBF) dengan mekanisme notifikasi otomatis untuk keluarga, namun cakupan sistem tersebut masih terbatas pada indikator vital fisik seperti tekanan darah, kadar gula darah, dan indeks massa tubuh, tanpa mencakup aspek kesehatan mental. Pada penelitian sebelumnya yang lain, penulis juga telah mengembangkan sistem pakar berbasis metode forward chaining untuk deteksi dini gangguan kejiwaan secara umum, namun sistem tersebut belum dirancang khusus untuk karakteristik populasi lansia maupun terintegrasi dengan ekosistem aplikasi mobile dan mekanisme notifikasi caregiver. Penelitian ini bertujuan menganalisis kebutuhan fungsional dan non-fungsional bagi pengembangan modul deteksi dini gangguan mental lansia sebagai ekstensi dari aplikasi SiLansia, guna menutup kesenjangan antara kedua penelitian sebelumnya tersebut. Metode yang digunakan adalah studi literatur terhadap instrumen skrining kesehatan mental lansia dan konsep beban caregiver, dipadukan dengan tinjauan terhadap arsitektur dan hasil pengujian SiLansia yang telah tervalidasi, yang kemudian dipetakan menjadi kebutuhan sistem melalui identifikasi aktor dan pemetaan kebutuhan fungsional maupun non-fungsional. Hasil penelitian menunjukkan bahwa modul yang dibutuhkan mencakup skrining gejala psikologis berbasis instrumen Geriatric Depression Scale (GDS), analisis risiko, serta perluasan mekanisme notifikasi berbasis Firebase Cloud Messaging (FCM)
Prediksi Intensitas Radiasi Matahari Menggunakan Random Forest Berbasis Data Cuaca BMKG Fauzan Natsir; Redo Abeputra Sihombing; Abdurahman; Triana Dewi Salma; Muhammad Hidayat
Journal of Information Technology, Software Engineering and Computer Science (ITSECS) Vol. 4 No. 1 (2025): Volume 4 Number 1 January 2026
Publisher : PT. Tech Cart Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58602/itsecs.v4i1.391

Abstract

Lama penyinaran matahari merupakan parameter cuaca penting yang berkaitan dengan ketersediaan energi surya dan sektor pertanian. Penelitian ini mengembangkan model prediksi menggunakan metode Random Forest berbasis data BMKG dari dataset Climate Data Daily IDN (Kaggle), yang mencakup observasi harian pada periode 2018 hingga 2022 di lima stasiun cuaca di Pulau Jawa. Fitur masukan yang digunakan meliputi suhu, kelembaban, curah hujan, kecepatan angin, dan informasi waktu, dengan total 4.879 titik data. Model Random Forest menghasilkan R² = 0,358, RMSE = 2,372 jam, dan MAE = 1,889 jam, mengungguli regresi linier berganda (R² = 0,235) dan regresi vektor dukungan (R² = 0,347). Kelembaban relatif, posisi hari dalam tahun, dan selisih suhu harian merupakan prediktor yang paling berpengaruh. Keterbatasan utama penelitian ini adalah tidak tersedianya data tutupan awan, variabel fisik yang paling dominan dalam menentukan lama penyinaran matahari.
Co-Authors Abdurahman Achmad Fauzi Achmad Fauzi Ade Kurnia Solihin Agung Budi Laksono Aisyah Mutia Dawis Andi Dwi Pangestu Anggi Dimas Prasetyo Hendar Linden Anuj Kumar Aprilia Sulistyohati Bakhtiar, Muhammad Yusuf Desika, Ria Ega Shela Marsiani Farhan Maulana Arramsy Fauzan Natsir Gilang Ryan Fernandes Halimatushadi'ah Halimatushadi'ah Heriyati Heriyati Hidayat, Muhammad Hoiriyah, . Ika Mei Lina Imam Himawan Khoirul Umam, Muhammad Azkia Lukas Arief Prasety Lukas Arief Prasetyo marsiani, ega shela Maududi, Roni Al Mei Lilis Susanti Millati Izzatillah Muhammad Yusuf Bakhtiar Nandang Suwela Prasetyo, Lukas Arief Rizki Fais Mubarok Roni Al Maududi Sari, Rita Karmila Setiawan, Muhammad Rofi Sulistyohati, Aprilia Triana Dewi Salma Triyadi Triyadi Triyadi Triyadi Triyadi Triyadi, Triyadi