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All Journal Jurnal Buana Informatika Bulletin of Electrical Engineering and Informatics JURNAL ELEKTRO Scientific Journal of Informatics Register: Jurnal Ilmiah Teknologi Sistem Informasi Jurnal Pemberdayaan Masyarakat Madani (JPMM) JIKO (Jurnal Informatika dan Komputer) INOVTEK Polbeng - Seri Informatika MITRA: Jurnal Pemberdayaan Masyarakat Indonesian Journal of Computing and Modeling JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) CCIT (Creative Communication and Innovative Technology) Journal Jurnal Mantik Jurnal Pelayanan dan Pengabdian Masyarakat (Pamas) Journal of Information Systems and Informatics Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Jurnal Mnemonic Jurnal Tekinkom (Teknik Informasi dan Komputer) JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Computer Science and Information Technologies Jurnal Pengabdian Masyarakat Asia SPEKTA (Jurnal Pengabdian Kepada Masyarakat : Teknologi dan Aplikasi) Jurnal Restikom : Riset Teknik Informatika dan Komputer International Journal Software Engineering and Computer Science (IJSECS) Jurnal Nasional Teknik Elektro dan Teknologi Informasi Jurnal INFOTEL Jurnal Pendidikan Teknologi Informasi (JUKANTI) Jurnal Indonesia : Manajemen Informatika dan Komunikasi Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) International Journal of Information Technology and Business Journal Social Engagement: Jurnal Pengabdian Masyarakat INOVTEK Polbeng - Seri Informatika JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
Christine Dewi
Universitas Kristen Satya Wacana
Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Public Health Social Sciences Other

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Implementation of face recognition using Python Febrian Wahyu Christanto; Husnul Arifin; Christine Dewi; Teguh Prasandy
Computer Science and Information Technologies Vol 7, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v7i1.p1-9

Abstract

Artificial intelligence (AI)-based technology systems are developing rapidly. Along with technological development the number of criminal cases caused by facial forgery is also growing. Cases of theft and housebreaking with fake photos are a common problem in Semarang. In 2022–2023 the number of cases of theft and housebreaking reached 372,965 with a crime risk level of 137/100,000 people. To overcome this problem the facial recognition system used in the door security system uses digital image processing. This method works by imitating how nerve cells communicate with interconnected neurons, or more precisely, how artificial neural networks function in humans. As training data, image capture and facial recognition are carried out using a webcam and the Python programming language with the TensorFlow library. The image processing algorithm uses 400 facial images with an accuracy rate of 95%. However further development is needed to improve the efficiency and accuracy of the system to produce better results.
Python-based stock price prediction using backpropagation neural networks: a case study on ANTM Prind Triajeng Pungkasanti; Febrian Wahyu Christanto; Fadhilatut Tasyriqul Hajjas Sabat; Christine Dewi; Eryan Ahmad Firdaus
Bulletin of Electrical Engineering and Informatics Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i1.9760

Abstract

Accurate stock price prediction is critical for informed investment decisions. Today, stock trading has become a popular option as a source of income among people, due to its potential for rapid gains in a short time, but, due to fluctuating stock prices, it can cause great losses in exchange. This study aims to forecast the closing price using the backpropagation neural network algorithm so that it can be used as a decision support for potential investors and traders in this research, the system was built using the Python programming language, and the stock price data used were shares of the company Aneka Tambang Tbk (ANTM). The results of this research are root mean squared error (RMSE) values, additional labels for prediction results, and graphs for comparison of the original data with the predicted data. Based on the testing result, the best value of RMSE is 3.786, the mean absolute percentage error (MAPE) value is 0.001 which indicates that the prediction results are very close to the actual value.
Implementasi Sistem Pengambilan Nomor Antrean Online dengan Pendekatan Waterfall dan Keamanan MFA Adri Agustinus Bleskadit; Christine Dewi
Journal of Information System Research (JOSH) Vol 6 No 2 (2025): January 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i2.6187

Abstract

In the digital era, information technology plays an important role in increasing the efficiency of various sectors, including public services. One of the problems faced by the XYZ office in tax services is taking queue numbers. Long queues often cause long waiting times for visitors and reduce company efficiency, which ultimately impacts public satisfaction and perceptions of public services. An efficient queuing system not only improves the user experience but also the productivity of the institution. However, manual systems are often slow, prone to errors, and less flexible, so digital-based solutions are needed. This research aims to design a website-based queue number retrieval system using the waterfall method. To ensure the security of user data, the system is equipped with a Multi-Factor Authentication (MFA) feature, which increases the protection of user data from unauthorized access. This system was built using the PHP programming language and is supported by the XAMPP device as a local server. Tools such as Entity Relationship Diagrams (ERD) and Unified Modeling Language (UML) are used to design data structures and system flows effectively. It is hoped that this research will provide a practical solution to make it easier to collect queue numbers online, reduce waiting times, and increase user satisfaction and safety at the XYZ office.
Classification of Skin Diseases Using YOLOv11 Liputra Pronimus Tappi; Christine Dewi
Journal of Innovation and Technology Polbeng Series on Informatics (INOVTEK Polbeng - Seri Informatika) Vol. 10 No. 2 (2025): July
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/9zv65764

Abstract

The skin, as the largest organ in the human body, is susceptible to various diseases that can be transmitted through direct contact or environmental exposure. Early detection of conditions such as cancer is crucial for effective treatment. This study implements the YOLOv11 algorithm to classify four types of skin diseases: Actinic Keratosis, Basal Cell Carcinoma, Melanocytic Nevus, and Melanoma. Using a Kaggle dataset of 2,000 images (500 per class), the images were processed by resizing them to 640×640 pixels and applying augmentation techniques (flipping, rotation, lighting adjustments) to enhance model robustness. The data was split into training (85%), validation (10%), and testing (5%). Model training on Google Colab (T4 GPU, 100 epochs) achieved an overall accuracy of 79%. Evaluation metrics showed strong results for Actinic Keratosis (precision=0.92, recall=0.92, F1=0.92) but lower performance for Melanoma (recall=0.59), likely due to class imbalance. Aggregate metrics indicated precision=0.80, recall=0.73, and F1=0.76, demonstrating reliable detection despite uneven performance across disease types. The main limitations include: a limited dataset size affecting model generalization; variability in image quality and lighting; and bias toward certain classes.
Quality of Service Analysis on the Steam Link Platform as an Alternative to Online Gaming Technology Gabriel Patandung; Christine Dewi
Journal of Innovation and Technology Polbeng Series on Informatics (INOVTEK Polbeng - Seri Informatika) Vol. 10 No. 2 (2025): July
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/rk0s3v40

Abstract

Cloud gaming is a modern gaming option that has emerged as a result of technological advancements, offering users the convenience of playing games without requiring high-end hardware. This study aims to analyze the Quality of Service (QoS) of the Steam Link platform as an alternative to traditional cloud gaming technology. The evaluation focuses on two types of clients—Android devices and laptops—using quantitative methods, benchmarking, and TIPHON standardization. The tested parameters include throughput, delay, frame rate, and the usage of CPU, GPU, and RAM. Experiments were conducted for 15 minutes across three game genres (FPS, Racing, and Open World), using resolutions of 720p and 1080p, and bandwidth levels of 30, 40, and 50 Mbps. Each scenario was tested three times. The host device used a PC with an Intel Core i5-6400 processor and GTX 1070 GPU, while the clients included a Xiaomi 12 smartphone and an Acer TravelMate i3-1115G4 laptop. Test results showed throughput ranging from 14.542 to 33.920 Mbps, delay between 1.382 and 1.721 ms, and frame rates stable between 30 and 60 FPS. CPU and RAM usage remained under 30%, indicating efficient performance. However, issues such as host stuttering and performance differences between clients were observed. According to TIPHON standards, both throughput and delay were rated as very good. With a stable 50 Mbps network connection, Steam Link proves to be a practical and affordable alternative for cloud gaming.
PEMANFAATAN ALGORITMA YOLOV11-POSE UNTUK SISTEM DETEKSI ORANG JATUH BERBASIS KAMERA THERMAL Regita Manipa; Christine Dewi
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 11, No 2 (2026)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v11i2.7813

Abstract

Jatuh merupakan masalah serius pada berbagai kelompok usia yang dapat menyebabkan cedera fisik, mulai dari ringan hingga fatal. Deteksi jatuh menjadi aspek penting dalam pengembangan sistem pemantauan yang efektif dan andal. Penelitian ini mengevaluasi model YOLOv11-Pose yang dilatih menggunakan data kamera termal dari dataset OpenThermalPose di lingkungan dalam dan luar ruangan. Pengujian dilakukan dengan fungsi deteksi berbasis dua kondisi: rasio dimensi tubuh dan orientasi terhadap lantai. Dari lima varian yang diuji, YOLOv11-M menunjukkan kinerja terbaik dengan nilai mean Average Precision (mAP) sebesar 0,994 untuk kotak pembatas dan 0,855 untuk estimasi pose. Meskipun model mampu mendeteksi posisi tubuh secara akurat saat tidak terjadi jatuh, performanya terbatas akibat kurangnya data citra yang merepresentasikan posisi jatuh, sehingga menghambat evaluasi secara menyeluruh. Hasil ini menunjukkan potensi YOLOv11-Pose dalam mendeteksi kejadian jatuh serta memberikan wawasan untuk pengembangan sistem keselamatan berbasis kamera termal di masa depan.
OPTIMALISASI MODEL DETEKSI DINI DIABETES DENGAN TEKNIK FEATURES SELECTION Lanyta Setyani Gunawan; Christine Dewi
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 10, No 4 (2025)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v10i4.7006

Abstract

Diabetes merupakan penyakit kronis yang dapat menyebabkan dampak serius jika tidak ditangani sejak dini, termasuk komplikasi seperti kerusakan organ dan penyakit kardiovaskular. Deteksi dini menggunakan teknologi machine learning menjadi salah satu kunci untuk pencegahan dan penanganan yang lebih efektif. Penelitian ini bertujuan untuk mengembangkan model prediksi risiko diabetes dengan menggunakan beberapa algoritma machine learning, seperti Random Forest, Naïve Bayes, Decision Tree, Logistic Regression, dan XGBoost. Dataset "Early Stage Diabetes Risk Prediction" dari UCI, yang terdiri dari 16 fitur dan 520 data, digunakan sebagai dasar pelatihan model. Beberapa teknik seleksi fitur, seperti Analisis Korelasi, Chi-Square, Information Gain, dan Fisher’s Score, diterapkan untuk mengidentifikasi fitur yang paling relevan dan mengurangi kompleksitas model. Evaluasi dilakukan menggunakan metrik seperti Accuracy, Precision, Recall, dan F1 Score. Hasil penelitian menunjukkan bahwa penerapan seleksi fitur secara signifikan meningkatkan performa model, menjadikannya jauh lebih baik dan akurat untuk mendukung deteksi dini risiko diabetes serta pengambilan keputusan medis yang lebih tepat dan responsif.
DETEKSI PENIPUAN KARTU KREDIT DENGAN MACHINE LEARNING DAN VISUALISASI INTERAKTIF BERBASIS STREAMLIT Ananda Dwi Erviana; Christine Dewi
JURNAL INFORMATIKA DAN KOMPUTER Vol 10, No 2 (2026): Juni 2026
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat - Universitas Teknologi Digital Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26798/jiko.v10i2.2709

Abstract

Penipuan kartu kredit merupakan ancaman serius bagi industri keuangan yang terus meningkat seiring pertumbuhan transaksi digital. Penelitian ini membahas pembangunan sistem deteksi penipuan kartu kredit menggunakan algoritma Decision Tree dan Random Forest. Penelitian ini membahas penerapan sistem untuk mendeteksi penipuan kartu kredit dengan menggunakan algoritma Decision Tree dan Random Forest. Kartu kredit kumpulan data. csv yang diambil dari Kaggle, berisi sekitar satu juta transaksi dengan 30 fitur dari Principal Component Analysis (PCA), digunakan sebagai data utama. Proses penelitian meliputi beberapa tahapan, yaitu pra-pemrosesan data (penghapusan duplikasi, eliminasi kolom Time, dan normalisasi Z-score pada fitur Amount), analisis eksploratif (EDA), pelatihan model dengan rasio pembagian 80:20 menggunakan stratifikasi, serta evaluasi menggunakan matriks akurasi, presisi, recall, F1-score, dan ROC-AUC. Hasil penelitian menunjukkan bahwa Random Forest memiliki kinerja terbaik dibandingkan Decision Tree pada seluruh matriks evaluasi. Selanjutnya, sistem ini diimplementasikan ke dalam aplikasi Streamlit yang menyediakan fitur untuk mengunggah dataset, visualisasi EDA, pelatihan model, perbandingan kinerja antar algoritma, serta prediksi transaksi baru secara real-time. Secara keseluruhan, sistem yang dikembangkan mampu mendeteksi transaksi berpotensi penipuan secara efektif dan menyajikannya dalam antarmuka yang interaktif serta mudah digunakan.
Co-Authors Adhi, Adeste Charisma Lumenvitha Aditya, Michael Rio Adri Agustinus Bleskadit Albertus Pramukti Narendra Ambrosius Sindu Ananda Dwi Erviana Andika, Rio Arya Angkur, Lusiana V.G Anjar Widhyo Sasongko Anugrah Theodorus Daeli Ari Wibawa Aria Hendrawan, Aria Bitra, Marcelino Charmelita, Pauelina Chen, Rung-Ching Christanto, Henoch Juli Christo Sidupa, Bertnaldy Dagha, Willyam Chrisna Umbu Denny Jean Cross Sihombing Dienda Rizkya Hayuningtyas Roosaputri Dita Madonna Simanjuntak Elizabeth Sri Lestari Emanuel Pabianan Eryan Ahmad Firdaus Fadhilatut Tasyriqul Hajjas Sabat Faisal Rahutomo Febrian Wahyu Christanto Frans Robert Bethony Gabriel Patandung Gallen Cakra Adhi Wibowo Gerald Edgard Laukon Glorya Maya Marcia Sapan Bethony Handayani, Sri Hendry Henoch Juli Christanto Henoch Juli Christanto Henoch Juli Christanto Hiuredhy, Davin Kurnia Husnul Arifin Jhosefhin, Nicola Van Robert Juli Christanto, Henoch Julius Victor Manuel Bata Kroons, Aquenov Alexandro Kumala Nindya Pramono Lanyta Setyani Gunawan Lim, Ricardo Jonathan Liputra Pronimus Tappi Manatap Dolok Lauro Mavish, Steven Nindya Pramono, Kumala Nindya Pramono Oktaviani, Gracelya Oky Dwi Nurhayati Pabianan, Emanuel Panja, Eben Patandung, Gabriel Pratama, Yoga Candra Adi Prind Triajeng Pungkasanti, Prind Triajeng Rahmat Gernowo Ramos Somya Regita Manipa Ria Cantika Larasati Rio Arya Andika sangga, harmanto Stephen Aprius Sutresno, Stephen Aprius Tanujaya, Matthew Tappi, Liputra Pronimus Teguh Prasandy Titis Handayani Valentina, Vierena Yeremia Yulianto Yerik Afrianto Singgalen Yoga Candra Adi Pratama Yulianto, Yeremia