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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika Swabumi (Suara Wawasan Sukabumi) : Ilmu Komputer, Manajemen, dan Sosial Bianglala Informatika : Jurnal Komputer dan Informatika Akademi Bina Sarana Informatika Yogyakarta Cakrawala : Jurnal Humaniora Bina Sarana Informatika JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING INOVTEK Polbeng - Seri Informatika JITK (Jurnal Ilmu Pengetahuan dan Komputer) IKRA-ITH Informatika : Jurnal Komputer dan Informatika J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Teknologi Informasi dan Terapan (J-TIT) INTI Nusa Mandiri JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Infortech Jurnal Sistem Komputer dan Informatika (JSON) Jurnal Teknologi Informatika dan Komputer Jurnal Sistem Informasi dan Sistem Komputer Jurnal Sistem Informasi RESOLUSI : REKAYASA TEKNIK INFORMATIKA DAN INFORMASI Akasia: Artikel Ilmiah Sistem Informasi Akuntansi JUSTIAN - Jurnal Sistem Informasi Akuntansi J-SAKTI (Jurnal Sains Komputer dan Informatika) IMTechno: Journal of Industrial Management and Technology Paradigma Jurnal Ilmiah Sistem Informasi dan Ilmu Komputer
Irmawati Carolina
Universitas Bina Sarana Informatika
Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Library & Information Science Social Sciences Other

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Clustering Koridor Transjakarta Berdasarkan Jumlah Penumpang Dengan Algoritma K-Means Supriyatna, Adi; Carolina, Irmawati; Janti, Suhar; Haidir, Ali
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 4, No 2 (2020): EDISI SEPTEMBER
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v4i2.259

Abstract

Transportation is one of the facilities that make it easy for humans to carry out activities to move places using vehicles that are driven by humans or machines. Based on data obtained from data.jakarta.go.id, the number of Transjakarta bus passengers in corridors 1 to 13 of 2017 amounted to 114,239,960, and in 2018 there were 121,918,964 passengers. The algorithm used in this research is K-Means Cluster, which is implemented using Microsoft Excel and Rapidminer Studio. The purpose of this study is to cluster Transjakarta corridors based on the number of passengers divided into 3 clusters: high, medium, and low. The results of data processing show that the Transjakarta corridor data cluster is based on the number of passengers using the K-Means cluster algorithm using Microsoft Excel and Rapidminer Studio to produce 3 clusters, namely cluster 1 with the highest number of passengers, one corridor, cluster 2 with the number of passengers being nine corridors and cluster 3 or 0 with a low number of passengers there are three corridors. The highest number of passengers is corridor one which serves the Blok M - Kota route, indicating that the Blok M - Kota route is the most used by Transjakarta passengers.
Clustering Koridor Transjakarta Berdasarkan Jumlah Penumpang Dengan Algoritma K-Means Supriyatna, Adi; Carolina, Irmawati; Janti, Suhar; Haidir, Ali
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 4, No 2 (2020): EDISI SEPTEMBER
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (978.801 KB) | DOI: 10.30645/j-sakti.v4i2.259

Abstract

Transportation is one of the facilities that make it easy for humans to carry out activities to move places using vehicles that are driven by humans or machines. Based on data obtained from data.jakarta.go.id, the number of Transjakarta bus passengers in corridors 1 to 13 of 2017 amounted to 114,239,960, and in 2018 there were 121,918,964 passengers. The algorithm used in this research is K-Means Cluster, which is implemented using Microsoft Excel and Rapidminer Studio. The purpose of this study is to cluster Transjakarta corridors based on the number of passengers divided into 3 clusters: high, medium, and low. The results of data processing show that the Transjakarta corridor data cluster is based on the number of passengers using the K-Means cluster algorithm using Microsoft Excel and Rapidminer Studio to produce 3 clusters, namely cluster 1 with the highest number of passengers, one corridor, cluster 2 with the number of passengers being nine corridors and cluster 3 or 0 with a low number of passengers there are three corridors. The highest number of passengers is corridor one which serves the Blok M - Kota route, indicating that the Blok M - Kota route is the most used by Transjakarta passengers.
Implementasi Sistem Informasi Manajemen Kontrak Beauty Advisor Berbasis Web Pada PT Sinergi Global Servis Rabbany Tricahya, Muhammad Amar; Carolina, Irmawati; Yulianto, Eko
Jurnal Sistem Informasi dan Sistem Komputer Vol 10 No 2 (2025): Vol 10 No 2 - 2025
Publisher : STIMIK Bina Bangsa Kendari

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51717/simkom.v10i2.922

Abstract

Pengelolaan data karyawan dan kontrak kerja merupakan aspek penting dalam mendukung operasional perusahaan, terutama di industri kosmetik dan retail. Penelitian ini bertujuan mengembangkan sistem informasi berbasis web untuk manajemen kontrak karyawan pada PT SINERGI GLOBAL SERVIS, anak perusahaan Martha Tilaar Group. Sistem dirancang untuk mendukung pengelolaan data karyawan, khususnya Beauty Advisor, serta proses pembuatan dan monitoring kontrak kerja. Metodologi yang digunakan adalah Software Development Life Cycle (SDLC), dengan implementasi backend menggunakan Lumen Framework dan database PostgreSQL, serta frontend menggunakan Vue.js. Sistem memanfaatkan teknologi Docker untuk efisiensi pengelolaan server. Fitur utama meliputi pembuatan kontrak otomatis, pengelolaan data karyawan, ekspor laporan format Excel dan PDF, serta integrasi evaluasi kinerja. Hasil menunjukkan sistem mampu meningkatkan efisiensi proses manajemen kontrak dan menyediakan solusi komprehensif pengelolaan sumber daya manusia yang efektif dan efisien.
Analisis Pengaruh Desain dan Fungsionalitas Website terhadap Kepuasan Pengguna Streetwear X Indonesia Menggunakan Pendekatan Model UTAUT2 Chairilica Renata Putri; Chelsea Dinda Azzahra; Gandini Kemala Rahmawati; Irmawati Carolina; Eko Yulianto
Jurnal ilmiah Sistem Informasi dan Ilmu Komputer Vol. 5 No. 3 (2025): November: Jurnal ilmiah Sistem Informasi dan Ilmu Komputer
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/juisik.v4i3.1369

Abstract

This study aims to analyze the impact of website design and functionality on user satisfaction of Streetwear X Indonesia, using the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) model. The background of this research is based on the importance of interface quality and user experience in enhancing satisfaction and the continued use of e-commerce websites, especially in the competitive streetwear industry. The independent variables analyzed include website design, which encompasses aesthetics, navigation, and visual consistency, as well as website functionality, which includes access speed, transaction ease, and interactivity. User satisfaction is the dependent variable, serving as the key indicator of the website's acceptance and user loyalty. This research employs a quantitative approach, with Partial Least Square Structural Equation Modeling (PLS-SEM) used to test the relationships between the variables and measure the impact of each factor on user satisfaction. Data was collected through questionnaires distributed to active users of the X Indonesia website, representing various user demographics. The analysis results show that both website design and functionality have a significant positive impact on user satisfaction, emphasizing the importance of both visual appeal and operational efficiency in shaping user experience. The findings offer practical implications for website managers, suggesting that focusing on developing an attractive interface, intuitive navigation, and responsive functionality can improve user experience. Therefore, optimizing design and functionality elements strategically can increase user loyalty, enhance their interaction experience, and promote the long-term sustainability of the X Indonesia website.
Model Prediktif Keterlambatan Pembayaran Mahasiswa Berbasis Seleksi Fitur dengan Particle Swarm Optimization Desvia, Yessica Fara; Suharjanti; Suhardjono; Irmawati Carolina; Resti Lia Andharsaputri
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 7 No. 2 (2025): Desember 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v7i2.8973

Abstract

Keterlambatan pembayaran biaya kuliah menjadi salah satu permasalahan krusial di perguruan tinggi swasta yang dapat berdampak pada risiko akademik, seperti cuti atau putus studi. Penelitian ini diarahkan untuk mengembangkan model prediktif dalam mengidentifikasi keterlambatan pembayaran oleh mahasiswa, dengan memanfaatkan algoritma klasifikasi Decision Tree dan Random Tree, serta menerapkan metode Particle Swarm Optimization (PSO) untuk proses seleksi fitur. Data yang digunakan dalam penelitian ini mencakup 15.697 mahasiswa, masing-masing memiliki enam atribut sebagai variabel prediktor serta satu atribut target yang menunjukkan status mahasiswa, yaitu aktif atau cuti. Tahapan penelitian mencakup pengumpulan data, pra-pemrosesan, klasifikasi, seleksi fitur, dan evaluasi model dilakukan dengan menggunakan metrik akurasi, serta kurva ROC dan nilai AUC. Hasil penelitian menunjukkan akurasi model mencapai 98,83%, dengan peningkatan signifikan AUC pada Random Tree dari 0,632 menjadi 0,825 setelah seleksi fitur menggunakan PSO. Temuan ini menunjukkan bahwa PSO efektif dalam meningkatkan performa model klasifikasi dan mengurangi kompleksitas fitur yang tidak relevan. Sistem prediktif yang dihasilkan dapat membantu institusi pendidikan dalam melakukan deteksi dini mahasiswa berisiko menunggak, sehingga memungkinkan pengambilan tindakan preventif dan intervensi lebih tepat sasaran untuk mendukung keberlangsungan akademik mahasiswa.
K-Nearest Neighbors Optimization using Particle Swarm Optimization in Selection Digital Payments Ridwansyah Ridwansyah; Resti Lia Andharsaputri; Yudhistira Yudhistira; Irmawati Carolina; Suharjanti Suharjanti
Jurnal Teknologi Informasi dan Terapan Vol 12 No 1 (2025): June
Publisher : Jurusan Teknologi Informasi Politeknik Negeri Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25047/jtit.v12i1.442

Abstract

Fintech developments have increased the use of digital payment systems such as OVO and GoPay. However, selecting a payment method that suits user preferences is still a challenge. This research proposes a combination of K-Nearest Neighbors (KNN) and Particle Swarm Optimization (PSO) to improve the classification accuracy of digital payment systems. The dataset used comes from a survey of Fintech users with factors such as ease of application, data security, cashback and customer service. KNN is used as a classification method, while PSO is applied for feature selection to improve model efficiency. Evaluation is carried out using accuracy, precision, recall, and AUC. The research results show that accuracy increased from 94.00% to 95.47% after optimization with PSO. The most influential factors are customer service, user employment and cashback. However, the AUC value remains 0.500, which shows that the model still has limitations in optimally differentiating categories. Further research is recommended to explore other algorithms such as Random Forest and SVM, as well as developing a machine learning-based digital payment recommendation system
Comparative Analysis of Multi-Classifier Models with Resampling Techniques for Imbalanced Student Graduation Prediction Carolina, Irmawati; Lia Andharsaputri, Resti; Suharjanti, Suharjanti; Prihatin, Titin; Nurdin, Hafis
Paradigma - Jurnal Komputer dan Informatika Vol. 28 No. 1 (2026): March 2026 Period
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/p.v28i1.11976

Abstract

Student graduation prediction supports early academic intervention but commonly suffers from class imbalance, where on-time graduates dominate the dataset. This study evaluates five classifiers—Random Forest (RF), XGBoost, Logistic Regression (LR), k-Nearest Neighbors (k-NN), and Gaussian Naïve Bayes (GNB)—under five class-imbalance handling scenarios: Baseline (no resampling), Random Undersampling (RUS), SMOTE, ADASYN, and Borderline-SMOTE. Experiments were conducted on 796 student records (10 attributes) with an imbalanced distribution (634 on-time vs. 162 not on-time; ratio 1:3.9) using Stratified 5-Fold Cross-Validation. Performance was assessed using confusion-matrix metrics and AUC-ROC to reflect minority-class detection. Under baseline, RF achieved the highest accuracy (0.873) but limited minority recall (0.573), confirming majority-class bias. Resampling consistently improved minority recall across models; for example, LR recall increased to 0.802 with RUS, while GNB reached 0.833 with ADASYN, although accuracy decreased due to the sensitivity–specificity trade-off. Overall, RF and XGBoost showed the most stable discrimination across resampling scenarios based on AUC (RF: 0.870–0.883; XGBoost: 0.847–0.866). The main contribution is a systematic, reproducible comparative evaluation of classifier–resampling combinations for imbalanced graduation prediction, providing practical guidance for selecting robust models to identify students at risk of delayed graduation.  
SIMPLE ARTIFICIAL INTELLIGENCE APPLICATION FOR CLASSIFYING HOUSEHOLD WASTE AT THE NEIGHBORHOOD WASTE BANK Irmawati Carolina; Mari Rahmawati; Al Ghoni Achmed. J; Arifin Salam; M. Arif Budiman; M. Daffa Ramadhani
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 4 (2026): JITK Issue May 2026
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i4.8297

Abstract

Waste management remains a critical environmental issue globally, including in Indonesia, where increasing household waste generation creates significant environmental and social challenges, particularly at the neighborhood level. In community-based Waste Banks, manual sorting processes are often inconsistent due to limited human resources and varying levels of public understanding of waste categories. This study aims to develop and evaluate a lightweight, web-based real-time waste detection and classification system to support community-level waste management. The proposed system utilizes the YOLOv8 object detection architecture implemented through the Ultralytics framework with PyTorch as the deep learning backend, integrated with OpenCV for real-time video processing and Streamlit for web-based deployment. The dataset consists of approximately 9,200 annotated images across 24 waste categories, divided into training, validation, and testing sets, with data augmentation applied to improve robustness. Model performance was evaluated using precision, recall, and mean Average Precision at IoU 0.5 (mAP@0.5). The results demonstrate high detection performance, achieving 99.5% mAP@0.5, 99.4% precision, and 100.0% recall, while maintaining stable real-time detection under varying lighting conditions. However, these results are obtained under relatively controlled dataset conditions; therefore, further evaluation in more diverse real-world environments is necessary to ensure generalization capability. The system enables multi-object detection without requiring specialized hardware, making it accessible for neighborhood-level Waste Banks and providing a practical solution for community-based waste management.
Sistem Pendukung Keputusan Pemilihan Smartphone Menggunakan Metode SAW Berbasis Web Irmawati Carolina; Aryani Rahayu
IMTechno: Journal of Industrial Management and Technology Vol. 7 No. 2 (2026): Vol. 7 No 2 (2026) : Juli 2026
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/imtechno.v7i2.12791

Abstract

The rapid development of information technology has led to an increase in the production and diversity of smartphones available in the market. This condition often makes it difficult for consumers to select smartphones that meet their needs and budget constraints. To support this decision-making process, a system capable of providing objective recommendations based on measurable criteria is required. This study aims to develop a web-based Decision Support System (DSS) by implementing the Simple Additive Weighting (SAW) method for smartphone selection. The SAW method is chosen due to its ability to generate rankings that are simple, efficient, and easy to understand. The criteria used in the system include RAM, battery capacity, internal storage, camera quality, price, and performance score (Antutu). Smartphone data are obtained from reliable secondary sources. The implementation results indicate that the system is capable of presenting smartphone recommendations based on the highest preference values in accordance with the predefined criteria weights. This system is expected to assist users in obtaining more rational and structured information when determining the most suitable smartphone choice.
Co-Authors Adi Supriyatna Adjat Sudradjat Agus Salim Al Ghoni Achmed. J Ali Haidir Aminudin, Aam Anus Wuryanto Arief Rusman Arifin Salam Aryani Rahayu Baginda Oloan Lubis Chairilica Renata Putri Chelsea Dinda Azzahra Desvia, Yessica Fara Eko Yulianto Fitra Hariman Hakim Frans Edward Schaduw Gandini Kemala Rahmawati Geo Halomoan Simanjuntak Hafis Nurdin Hendri Hendri Henny Destiana Ikhwan Akbar Intan Sarwono Putri Kresna Ramanda Lita Sari Marita M. Arif Budiman M. Daffa Ramadhani Mari Rahmawati Melan Susanti Nurul Afni Puspa Vicenna Barulah Yudha Rabbany Tricahya, Muhammad Amar Rachman Komarudin Rachman Komarudin Resti Lia Andharsaputri Ridwansyah Ridwansyah Ridwansyah Ridwansyah Rusma Insan Nurachim Setianingsih Setianingsih, Setianingsih Suhar Janti Suhardjono S suharjanti Suharjanti Suharjanti Vivi Maria Wina Widiati Yana Iqbal Maulana Yudhistira Yudhistira