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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Sebatik JITTER (Jurnal Ilmiah Teknologi Informasi Terapan) J-SAKTI (Jurnal Sains Komputer dan Informatika) IJISTECH (International Journal Of Information System & Technology) JURTEKSI MIND (Multimedia Artificial Intelligent Networking Database) Journal Aptisi Transactions on Technopreneurship (ATT) METIK JURNAL JURNAL IT: Media Infromasi STMIK Handayani Makassar Building of Informatics, Technology and Science INFOKUM Brahmana : Jurnal Penerapan Kecerdasan Buatan TEPIAN IJISTECH Buletin Poltanesa J-SAKTI (Jurnal Sains Komputer dan Informatika) Informatika Wicida Jurnal Teknik Informatika Journal of Artificial Intelligence and Engineering Applications (JAIEA) Journal of Pedagogy and Education Science Journal of Informatics and Electronics Engineering TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi BEduManageRs Journal : Borneo Educational Management and Research Journal Information Technology Studies Journal Research in Education, Technology, and Multiculture
Heny Pratiwi
STMIK Widya Cipta Dharma
Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mathematics Mechanical Engineering Social Sciences Other

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Performance Analysis of the CP-SAT Algorithm for Practicum Scheduling Optimization Using Google OR-Tools Novandra Satria Winata; Heny Pratiwi; Aisyah Fajriantini
Research in Education, Technology, and Multiculture Vol 5, No 3 (2026): Research in Education, Technology, and Multiculture
Publisher : Institute of Multidisciplinary Research and Community Service

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61436/rietm/v5i3.pp172-188

Abstract

Laboratory practicum scheduling at higher education institutions involving multiple study programs, limited laboratory facilities, and complex theory class constraints constitutes a combinatorial optimization problem classified as NP-Hard. Previous studies on academic scheduling have applied various meta-heuristic and exact methods; however, few have simultaneously integrated laboratory specialization rules, multi-credit session contiguity, and theory schedule blocking within a single optimization framework. This study designs and implements an automated practicum scheduling system based on the Constraint Programming with Boolean Satisfiability (CP-SAT) method using Google OR-Tools to address the scheduling challenges at STMIK Widya Cipta Dharma. A quantitative optimization approach was employed, encompassing six systematic stages: data collection, requirements analysis, Set Theory-based data preprocessing, CP-SAT mathematical model formulation with five hard constraints and a hierarchical penalty objective function, algorithm execution, and five-aspect verification testing. The dataset comprises 22 practicum courses, 63 groups, 1,327 students, 5 laboratories, and 129 theory schedule blocking entries. The computational environment utilized an AMD Ryzen 7 8845HS processor (16 logical cores, 16 GB RAM) running Python 3.14.3 with OR-Tools 9.15.6755 on Windows 11. The CP-SAT solver processed 17,010 Boolean decision variables and achieved OPTIMAL status in 1.65 seconds, producing 102 conflict-free sessions with 100% compliance across all hard constraints and effective suppression of Saturday scheduling (Z=2.0). Resilience testing across three realistic scenarios confirmed consistent OPTIMAL status. Scalability stress testing from 129 to 329 theory blocks (26.9%–68.5% saturation) demonstrated graceful performance degradation, with the solver maintaining OPTIMAL status throughout, though objective values increased from Z=2.0 to Z=52.0 at highest saturation. Post-optimization field audit revealed that discrepancies between computed and actual schedules stem from uncoordinated student-initiated group swaps rather than algorithmic errors, highlighting the need for institutional swap-management protocols to preserve schedule optimality. Keywords: Constraint programming, CP-SAT, Laboratory scheduling, Google OR-Tools, Timetabling.
Application of K-Means Algorithm for Segmentation Analysis of Youtube Viewers in Indonesia Ryan Artanto Halim; Heny Pratiwi; Azahari Azahari
INFOKUM Vol. 13 No. 03 (2025): Infokum
Publisher : Sean Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58471/infokum.v13i03.2850

Abstract

The application of K-Means as a clustering method in segmentation analysis is common. However, academic research on YouTube audience segmentation in Indonesia is still limited. YouTube audiences in Indonesia are diverse, ranging from entertainment, education, to news, so more in-depth analysis is needed to identify user segments more specifically. YouTube audience segmentation can provide a deeper understanding of people's video consumption behavior. This understanding can help content creators and digital industry players develop more effective content strategies. K-Means was chosen as the clustering method in this study because it can group YouTube viewers in Indonesia based on their interaction patterns with YouTube content. In addition, K-Means' ability to handle large data is suitable for segmenting platforms with a large number of users such as YouTube. This research uses three main features, namely views, duration, and engagement rate to group viewers into five clusters. Cluster evaluation using Silhouette Score (0.3445), Davies-Bouldin Index (0.9576), and Calinski-Harabasz Index (481.4730) shows that the resulting segmentation is of good quality. The analysis shows that there are differences in video consumption patterns across clusters, reflecting variations in viewer preferences and engagement levels.
Eye Disease Classification Using Convolutional Neural Network (CNN) with Web-based MobileNetV2 Architecture Muhammad Fahriawan; Heny Pratiwi; Bartolomius Harpad
INFOKUM Vol. 13 No. 03 (2025): Infokum
Publisher : Sean Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58471/infokum.v13i03.2851

Abstract

The high prevalence of preventable eye diseases, such as cataracts, glaucoma, and diabetic retinopathy, emphasizes the importance of accessible and efficient diagnostic solutions. This research aims to develop a web-based eye disease classification system using a lightweight Convolutional Neural Network (CNN) architecture, MobileNetV2, to overcome computational limitations in real-time applications. CRISP-DM methodology is applied, including dataset preparation, transfer learning with MobileNetV2 and VGG16, model evaluation, and implementation using Flask. The dataset from Kaggle consisting of 4,217 eye fundus images with four classes (cataract, glaucoma, diabetic retinopathy, and normal) was divided into 80% training, 10% validation, and 10% testing. Data augmentation and normalization were performed to improve model generalization. The results showed MobileNetV2 achieved the highest accuracy (90.14%) with low computational requirements, outperforming VGG16 (89.66%) and CNN (86.78%). MobileNetV2 displays balanced precision (89-99%), recall (74-96%), and F1-score (81-99%) across all classes, especially excelling in diabetic retinopathy detection. Its efficiency on resource-constrained environments makes it ideal for web integration. The developed Flask-based application allows users to upload images for instant classification, bridging the healthcare access gap. This research proves the effectiveness of MobileNetV2 in combining high accuracy and computational efficiency, offering a scalable solution for early screening of eye diseases, especially in remote areas.
Visualisasi Interaksi Habitat Hewan dalam Lingkungan WebAR sebagai Media Pembelajaran Biologi Atventitus Etwin Loho; Heny Pratiwi; Jundro Daud Hasiholan
TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi Vol 5 No 2 (2025): TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/tamika.Vol5No2.pp478-485

Abstract

The study aimed to develop an interactive learning medium based on Web-based Augmented Reality (WebAR) markerless to visualize various animal habitats as part of Science education in elementary schools. The research employed the Multimedia Development Life Cycle (MDLC) method, which consists of six stages: concept, design, material collecting, assembly, testing, and distribution. The developed WebAR application allows students to explore 3D dioramas of animal habitats directly through a browser without additional installation. A beta test was conducted with 10 fifth-grade students from SDN 024 Samarinda using a Likert scale questionnaire that covered accessibility, visual display, interactivity, and user satisfaction. The test results showed an average score of 82.8%, indicating that the developed medium is well-received and effective in increasing students' motivation and concept understanding. This markerless WebAR medium provides a more immersive and contextual learning experience compared to conventional two-dimensional media. Future research could expand the number of respondents, test in different learning contexts, and add interactive features such as quizzes or narration to enhance learning engagement.
Implementasi Bot WhatsApp untuk Layanan Informasi Frontline: Studi Kasus: STMIK WICIDA Muhammad Sadam Saktia Putra; Azahari Azahari; Heny Pratiwi
TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi Vol 5 No 2 (2025): TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/tamika.Vol5No2.pp320-326

Abstract

This study implemented a WhatsApp bot as a frontline information service at STMIK Widya Cipta Dharma (WICIDA). The main problems were the high burden of repetitive questions, limited service hours, and inconsistent responses. WhatsApp was chosen because of its high adoption rate and support for real-time communication. The study included needs analysis, bot architecture design, Node.js-based development, knowledge base integration, and performance evaluation. The results showed that the bot was able to answer 87.4% of questions correctly, reduce staff workload by 56%, and speed up response time to <3 seconds. These findings demonstrate that the WhatsApp bot is effective as a scalable solution to improve the quality of educational information services.
Analisis Sentimen Orang Tua Murid Baru Terhadap SMPN 40 Samarinda pada SPMB 2025 Menggunakan Algoritma Naïve Bayes Resifa Ananta Putra; Heny Pratiwi; Ahmad Abul Khair
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 9 No. 2 (2025): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/jmika.Vol9No2.pp292-299

Abstract

The New Student Admission Selection (SPMB) plays an essential role in ensuring equal educational access in Indonesia. However, during SPMB 2025 at SMPN 40 Samarinda, many candidates living nearby did not choose the school as their first preference, suggesting that perceptions and school image significantly influenced their choices. This study aims to analyze new student parents sentiments toward SMPN 40 Samarinda using the Naïve Bayes algorithm combined with the Term Frequency–Inverse Document Frequency (TF-IDF) technique. Data were collected from 42 respondents and categorized into positive, neutral, and negative sentiments. The model achieved an accuracy of 86%, precision of 56%, and recall of 63%, showing that Naïve Bayes performs effectively on limited data, though less sensitive to minority classes. The analysis revealed that most parents expressed positive perceptions, indicating growing trust that SMPN 40 Samarinda can support students’ character development. These findings emphasize the importance of strengthening school image and service quality while highlighting the potential of machine learning–based sentiment analysis as a data-driven approach to understanding educational perceptions.
Analisis Data Viewer Youtube Untuk Menentukan Waktu Terbaik Live Streaming Game Menggunakan Decision Tree Akhmad Rizky Fahrozy; Heny Pratiwi; Hanifah Ekawati
Journal of Informatics and Electronics Engineering Vol. 6 No. 01 (2026): Juni 2026
Publisher : Unit Penelitian dan Pengabdian kepada Masyarakat Politeknik TEDC Bandung Jl. Pesantren Km 2 Cibabat Cimahi Utara – Cimahi 40513 Jawa Barat – Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70428/jiee.v6i01.1577

Abstract

Permasalahan dalam penelitian ini adalah belum adanya acuan yang jelas dalam menentukan waktu optimal untuk melakukan live streaming yang dapat menghasilkan tingkat interaksi pengguna yang tinggi. Oleh karena itu, penelitian ini bertujuan untuk menentukan waktu terbaik dalam melakukan live streaming berdasarkan data aktivitas viewer pada platform YouTube Studio. Metode yang digunakan adalah Decision Tree dengan pendekatan CRISP-DM yang meliputi enam tahapan, yaitu business understanding, data understanding, data preparation, modeling, evaluation, dan deployment. Dataset yang digunakan berjumlah 50 data, kemudian setelah dilakukan proses pembersihan menjadi 46 data yang valid untuk dianalisis. Hasil penelitian ini menunjukkan bahwa model Decision Tree mampu mengklasifikasikan performa live streaming ke dalam kategori rendah, sedang, dan tinggi dengan tingkat akurasi sebesar 0.60. Variabel yang paling berpengaruh adalah interaksi pengguna seperti chat, reaksi, dan durasi tonton. Berdasarkan hasil analisis, diperoleh waktu optimal untuk melakukan live streaming yaitu pada hari Selasa pukul 00:00 dengan kategori performa tinggi. Dengan demikian, dapat disimpulkan bahwa waktu memiliki pengaruh terhadap tingkat engagement pengguna, dan penelitian ini mampu memberikan rekomendasi waktu live streaming yang optimal berbasis data untuk membantu content creator dalam meningkatkan performa siaran.
IMPLEMENTASI SISTEM KEAMANAN RUMAH CERDAS BERBASIS IOT MENGGUNAKAN ESP32-CAM DENGAN PENGIRIMAN NOTIFIKASI GAMBAR SECARA REAL-TIME Kristianus Catur Prasetya Ajang; Azahari Azahari; Heny Pratiwi
Journal of Informatics and Electronics Engineering Vol. 6 No. 01 (2026): Juni 2026
Publisher : Unit Penelitian dan Pengabdian kepada Masyarakat Politeknik TEDC Bandung Jl. Pesantren Km 2 Cibabat Cimahi Utara – Cimahi 40513 Jawa Barat – Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70428/jiee.v6i01.1591

Abstract

Perkembangan teknologi Internet of Things (IoT) mendorong pengembangan sistem keamanan rumah yang lebih responsif, terintegrasi, dan mampu memberikan informasi secara cepat kepada pengguna. Penelitian ini bertujuan untuk mengimplementasikan sistem keamanan rumah cerdas berbasis ESP32-CAM yang mampu mendeteksi pergerakan dan mengirimkan notifikasi berupa gambar secara real-time melalui aplikasi Telegram. Sistem dirancang dengan memanfaatkan sensor Passive Infrared (PIR) sebagai pendeteksi gerakan yang berfungsi sebagai pemicu aktivasi kamera pada modul ESP32-CAM untuk mengambil gambar. Gambar kemudian dikirimkan kepada pengguna melalui jaringan internet menggunakan Telegram Bot API. Metode penelitian meliputi perancangan perangkat keras, pengembangan perangkat lunak, serta pengujian sistem secara langsung dengan mengukur kinerja deteksi dan waktu respons pengiriman data. Hasil pengujian menunjukkan bahwa sistem mampu mendeteksi pergerakan secara akurat dan mengirimkan notifikasi gambar dengan waktu tunda rata-rata kurang dari 5 detik, bergantung pada kondisi jaringan. Hasil ini menunjukkan bahwa integrasi ESP32-CAM dan Telegram Bot API dapat menjadi solusi sistem keamanan rumah berbasis IoT yang efektif, ekonomis, dan mudah diimplementasikan.
Analisis Pola Pembelian Konsumen di Kantin SMKN 11 Samarinda Menggunakan Algoritma Apriori Okvi Marsi Angela Claudia; Heny Pratiwi; Renni Mayasari
Journal of Informatics and Electronics Engineering Vol. 6 No. 01 (2026): Juni 2026
Publisher : Unit Penelitian dan Pengabdian kepada Masyarakat Politeknik TEDC Bandung Jl. Pesantren Km 2 Cibabat Cimahi Utara – Cimahi 40513 Jawa Barat – Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70428/jiee.v6i01.1593

Abstract

Perkembangan bisnis kuliner, termasuk usaha kantin sekolah, saat ini mengalami peningkatan yang cukup pesat sehingga persaingan antar pelaku usaha menjadi semakin ketat. Kondisi ini menuntut pengelola kantin untuk tidak hanya fokus pada kualitas produk dan pelayanan, tetapi juga mampu memahami perilaku serta kebutuhan konsumen secara lebih mendalam. Salah satu pendekatan yang dapat digunakan untuk mendukung pengambilan keputusan adalah pemanfaatan Data Mining, yaitu teknik pengolahan data untuk menemukan informasi penting yang tersembunyi dari data transaksi penjualan. Penelitian ini bertujuan untuk menganalisis pola pembelian konsumen di Kantin SMKN 11 Samarinda menggunakan Algoritma Apriori. Algoritma ini digunakan untuk mengidentifikasi hubungan asosiasi antar item menu berdasarkan data transaksi, sehingga dapat diketahui kombinasi produk yang sering dibeli secara bersamaan. Metode penelitian yang digunakan adalah metode kuantitatif dengan memanfaatkan data transaksi penjualan kantin selama periode tertentu. Tahapan penelitian meliputi pengumpulan data, seleksi dan pembersihan data, pembentukan itemset, serta perhitungan nilai support dan confidence untuk menghasilkan aturan asosiasi. Hasil penelitian menunjukkan bahwa Algoritma Apriori mampu menemukan pola pembelian konsumen berdasarkan keterkaitan antar menu yang sering muncul dalam satu transaksi, seperti kombinasi Ayam Geprek dengan Es Teh dan Ayam Geprek dengan Air Mineral. Berdasarkan hasil perhitungan diperoleh nilai support sebesar 13% dan confidence sebesar 33,3% pada aturan asosiasi tersebut. Nilai tersebut menunjukkan bahwa kombinasi produk tersebut cukup sering muncul dalam transaksi pembelian konsumen. Aturan asosiasi yang dihasilkan memberikan informasi yang bermanfaat bagi pengelola kantin dalam menentukan strategi promosi, pengaturan stok barang, penataan produk, serta peningkatan efektivitas pelayanan. Dengan demikian, penerapan Algoritma Apriori dapat membantu meningkatkan efisiensi pengelolaan penjualan serta mendukung pengambilan keputusan yang lebih tepat dan terarah pada usaha
Sistem Pakar Berbasis Web untuk Diagnosis Penanganan Pasca Panen Kelapa Sawit Menggunakan Metode Naive Bayes Heny Pratiwi; Muhammad Ibnu Sa'ad; Muhammad Alamsyah Zakaria
TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi Vol 4 No 2 (2024): TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/tamika.Vol4No2.pp259-267

Abstract

This study aims to design a web-based Expert System that is able to diagnose post-harvest handling of oil palm using the Naive Bayes method. In addition, this study also aims to explore optimal harvesting and post-harvest handling management in order to produce high-quality oil yields. This study was conducted at PT Sawit Sukses Sejahtera, the location where the experts work. Data collection was carried out through interviews with experts related to post-harvest handling of oil palm fruit, as well as literature studies to obtain data relevant to the research topic. The Naive Bayes method is used based on the probability found in the post-harvest handling process of oil palm, while system development follows the ESDLC (Expert System Development Life Cycle) methodology, which is the basis for designing and developing expert systems.
Co-Authors Abed Nego Achmad Sadzali Muftisjar Ade Maulana Anshari Adeputra, James Ahmad Abul Khair Ahmad Fahrijal Pukeng Ahmad Fahrijal Pukeng Ahmad Fahrijal Pukeng Ahmad Fajri Ahmad Rofiq Hakim Ahmad Sabirin Aisyah Fajrianti Aisyah Fajriantini Akhmad Rizky Fahrozy Aldianur Fajri Alysa Anggelia Y Amelia Yusnita Ananta Putra, Resifa Andi Yusika Rangan Anggra Prima Angreani, Fadillah Anwar, Rafidan Arsita Ashari Ramadani Atventitus Etwin Loho Azahari Azahari Azahari Azahari Azahari Azahari Bai' Fathur Rayhan Bartolomius Harpad Cembes, Yosefina Chandra Panca Wibawa Cintami Amanda Putri Damaya, Filio Angga Dana Aulia Rahman Daru Caraka Daud Yefkanius Nassa Daud, Jundro Dendy Kurniawan Dessy Purnamasari Dovist Calvino Ekawati, Hanifah Ekawati, Hanifah Eko Junirianto Fadjri Astra Ryan Sinurat Harianto, Kusno Haristyawan, Ivan I Made Borneo Setyawan Ita Arfyanti Julio Enrico Frans Frans Kristian Vandi Hermawan Kristianus Catur Prasetya Ajang Kusno Harianto Kusno Harianto Lamsi, Rahmadiansyah Zain M. Irwan Ukkas Irwan Ukkas Ukkas M.Ariya Parengrengi Muhammad Alamsyah Zakaria Muhammad Andrian Muhammad Fachri Sanjaya Muhammad Fadhilah Muhammad Fahmi Muhammad Fahmi Muhammad Fahriawan Muhammad Ibnu Sa'ad Muhammad Ibnu Sa'ad Muhammad Ibnu Saad Saad Muhammad Ibnu Sa’ad Muhammad Raihan Ramandha Putra Muhammad Rega Praduana Muhammad Sadam Saktia Putra Novandra Satria Winata NUR FITRIANI Nursobah, Nursobah Nurul Hikmah Okvi Marsi Angela Claudia Pahrudin, Pajar Pitrasacha Adytia Putra, Muhammad Sadam Saktia Putri Wulandari Renni Mayasari Resifa Ananta Putra Rifka Karin Afinda Rizky Zakaryya Rasyad Ryan Artanto Halim SA'AD, MUHAMMAD IBNU Saad, Muhammad Ibnu Salmon Salmon Salmon Sarifmata Purnomo Sa’ad, Muhammad Ibnu Shinta Palupi Suhariyadi, Yonatan Sururi, M Za’iem Susi Salviati Syamsuddin Mallala Syamsuddin Mallala Ulfa Nurfadhila W Wahyuni, W Wahyuni - Wahyuni Y Yunita Yunita Yunita Zakaria, Muhammad Alamsyah