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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JITK (Jurnal Ilmu Pengetahuan dan Komputer) KOPERTIP: Jurnal Ilmiah Manajemen Informatika dan Komputer METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi Jurnal ICT : Information Communication & Technology Infotekmesin Jurnal Informatika dan Rekayasa Elektronik Infotek : Jurnal Informatika dan Teknologi Jurnal Informatika Terpadu J-AKSI : JURNAL AKUNTANSI DAN SISTEM INFORMASI Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) Journal of Artificial Intelligence and Engineering Applications (JAIEA) Jurnal Ekonomi, Bisnis dan Manajemen Jurnal Sistem Informasi dan Teknologi (SINTEK) TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi AMMA : Jurnal Pengabdian Masyarakat Journal Innovations Computer Science Sintek Kuwera
Ade Irma Purnamasari
STMIK IKMI Cirebon
Humanities Automotive Engineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mechanical Engineering Nursing Social Sciences

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Pengembangan Pembelajaran Interaktif Sejarah Proklamasi Berbasis Game Mobile 2D Menggunakan Metode Game Development Life Cycle Habil Jabbal Firdausyi; Ade Irma Purnamasari; Irfan Ali; Indra Wiguna Marthanu; Fathurrohman Fathurrohman
Journal Innovations Computer Science Vol. 5 No. 1 (2026): May
Publisher : Yayasan Kawanad

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56347/jics.v5i1.413

Abstract

The History subject — particularly the Indonesian Proclamation of Independence — has long been delivered through conventional, one-directional methods that leave students disengaged and, in documented cases, visibly saturated. This study designed and developed an interactive digital learning medium as a direct response to that condition. The medium produced is a narrative-driven 2D Mobile Game, built using the Game Development Life Cycle (GDLC) across four phases: Concept, Pre-Production, Production, and Testing. The resulting prototype centers on a slide-controlling mechanism and a sequentially structured historical narrative, with an integrated quiz system embedded at the end of each story chapter. Validation by a Material Expert and a Media/Technology Expert placed the product in the "Highly Feasible" category, with an overall average score of 89.85% — a result that holds across both content accuracy and technical execution. Usability testing returned a System Usability Scale score of 81.0, rated "Excellent" and "Acceptable." These findings suggest the game is a credible alternative medium for reducing learning saturation and raising student engagement with History material.  
PENINGKATAN MODEL KLASIFIKASI SENTIMEN PENGGUNA APLIKASI TOMORO COFFEE MENGGUNAKAN ALGORITMA NAÏVE BAYES Dina Audina; Ade Irma Purnamasari; Agus Bahtiar; Edi Tohidi
Jurnal Informatika dan Rekayasa Elektronik Vol. 8 No. 1 (2025): JIRE APRIL 2025
Publisher : LPPM STMIK Lombok

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Abstract

Kemajuan teknologi informasi telah merevolusi cara bisnis berinteraksi dengan pelanggan melalui aplikasi mobile, termasuk dalam sektor makanan dan minuman. Aplikasi Tomoro Coffee menghadapi tantangan dalam mempertahankan kepuasan pengguna akibat keterbatasan fitur dan masalah teknis. Penelitian ini bertujuan untuk menerapkan algoritma Naïve Bayes guna meningkatkan model klasifikasi sentimen ulasan pengguna, menganalisis distribusi sentimen positif dan negatif beserta faktor utama yang memengaruhinya, serta mengevaluasi performa model berdasarkan akurasi, presisi, recall, dan F1-score. Data ulasan dikumpulkan dari Google Play Store dan diolah menggunakan metode Knowledge Discovery in Database (KDD), yang mencakup pembersihan data, tokenisasi, penghapusan stopword, stemming, serta ekstraksi fitur menggunakan Term Frequency-Inverse Document Frequency (TF-IDF). Hasil penelitian menunjukkan bahwa algoritma Naïve Bayes mencapai akurasi sebesar 90%, dengan presisi 91,3%, recall 87,3%, dan F1-score 88,7%. Temuan ini memberikan wawasan strategis bagi pengembang aplikasi dalam meningkatkan layanan dan fitur berdasarkan analisis sentimen pengguna. Dari hasil analisis, 64,4% ulasan tergolong positif, didominasi oleh komentar seperti "kopinya enak", sementara 35,6% ulasan negatif umumnya berisi keluhan teknis, seperti "tidak tersedia".
OPTIMASI KLASTERISASI PENERIMAAN PAJAK BUMI DAN BANGUNAN MENGGUNAKAN ALGORITMA K-MEDOIDS Febri Abdi Annur Dhuha; Ade Irma Purnamasari; Denni Pratama; Edi Tohidi; Edi Wahyudin
JURNAL AKUNTANSI DAN SISTEM INFORMASI Vol 7 No 1 (2026): Edisi Februari 2026
Publisher : Program Studi Akuntansi Fakultas Ekonomika dan Bisnis Universitas Majalengka

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31949/j-aksi.v7i1.16659

Abstract

Pajak Bumi dan Bangunan (PBB) merupakan komponen strategis dalam Pendapatan Asli Daerah (PAD) yang berperan penting dalam mendukung penyelenggaraan pembangunan dan pelayanan publik. Namun, heterogenitas data dan variasi karakteristik objek pajak menyebabkan pemerintah daerah mengalami kesulitan dalam memetakan potensi fiskal dan tingkat kepatuhan wajib pajak secara akurat. Penelitian ini bertujuan menganalisis efektivitas algoritma K-Medoids dalam mengelompokkan wajib pajak di Kecamatan Tanjung berdasarkan atribut numerik, yaitu luas tanah, luas bangunan, NJOP tanah, NJOP bangunan, dan nilai PBB tahun berjalan. Metode penelitian meliputi tahapan pengumpulan data, pra-pemrosesan, transformasi logaritmik, normalisasi, implementasi algoritma K-Medoids, serta evaluasi hasil klaster menggunakan metrik Silhouette Coefficient dan Davies–Bouldin Index. Proses komputasi dilakukan menggunakan Python dengan pustaka pyClustering dan scikit-learn. Hasil penelitian menunjukkan terbentuknya empat klaster wajib pajak dengan karakteristik berbeda: klaster aset besar berkontribusi rendah, klaster premium berkontribusi tinggi, klaster ekonomi rendah dengan pola pembayaran tidak stabil, dan klaster ekonomi menengah dengan kepatuhan cukup baik. Evaluasi kualitas model menghasilkan Silhouette Coefficient sebesar 0,4204 dan Davies–Bouldin Index sebesar 0,7893, yang menunjukkan struktur klaster cukup baik dan stabil. Temuan ini memberikan kontribusi empiris dalam mendukung optimalisasi pengelolaan PBB berbasis analitik, serta dapat digunakan sebagai dasar penyusunan strategi penagihan berbasis prioritas dan formulasi kebijakan fiskal yang lebih tepat sasaran.
ALGORITMA RANDOM FOREST UNTUK PREDIKSI STATUS PINJAMAN BERDASARKAN SKOR KREDIT Hadit Attaufiqqurrohman; Ade Irma Purnamasari; Denni Pratama; Nining Rahaningsih; Willy Prihartono
METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi Vol. 12 No. 1 (2026): Volume 12 Nomor 1 Tahun 2026
Publisher : Universitas Methodist Indonesia

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Abstract

The rapid development of financial technology has encouraged financial institutions to adopt data-driven credit scoring systems in order to minimize the risk of default. However, many loan eligibility prediction models still face challenges such as data imbalance (class imbalance) and the limited capability of traditional models to capture non-linear relationships among variables. This study aims to develop a loan status prediction model using the Random Forest algorithm combined with the Synthetic Minority Oversampling Technique (SMOTE) and One-Hot Encoding (OHE) to improve model accuracy and generalization capability. The data used in this study are secondary data obtained from the public Kaggle platform, consisting of 45,000 records with 14 demographic and financial attributes. The research method employs a supervised learning approach with several stages, including data acquisition and preprocessing (data cleaning, normalization, encoding, and data balancing), Random Forest model training, and performance evaluation using accuracy, precision, recall, F1-score, and AUC metrics. The results show that the combination of Random Forest, SMOTE, and OHE achieves high predictive performance, with an accuracy of 94.8%, precision of 95.6%, recall of 93.7%, F1-score of 94.6%, and an AUC value of 0.972. The most influential variables in loan status prediction are credit_score, person_income, and loan_amnt. This approach is proven to be effective in addressing data imbalance issues and improving classification accuracy in identifying creditworthy and non-creditworthy borrowers.
Optimization of Convolutional Neural Networks Using Resizing Techniques for Banana Leaf Disease Classification Aldiyansyah Kurniawan; Ade Irma Purnamasari; Denni Pratama; Edi Tohidi; Edi Wahyudin
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 2 (2026): February 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i2.1876

Abstract

Early and accurate identification of banana leaf diseases is essential for supporting digital agriculture, as visual symptoms often require rapid and reliable analysis. This study investigates the impact of three image resizing techniques squashing, letterboxing, and random resized crop on the performance of the MobileNetV2 architecture in classifying four categories of banana leaf images using the Banana Leaf Disease Dataset v4 consisting of 4,675 samples. The experiments were conducted using a transfer learning approach with an 80:10:10 data split, standardized normalization, and data augmentation. The results show that all resizing techniques achieved test accuracies above 92%. Squashing produced the highest accuracy and fastest training time, letterboxing demonstrated the most stable performance with the lowest validation loss, and random resized crop improved generalization to variations in object position. These findings confirm that resizing strategies significantly influence the stability and effectiveness of CNN models. Overall, MobileNetV2 proves capable of delivering accurate and efficient classification of banana leaf diseases when supported by an appropriate preprocessing pipeline. This study provides empirical evidence for developing image-based plant disease diagnosis systems within smart agriculture.
Improving the Education Development Contribution Payment Model at SMK Istiqomah Maruyung Using the C4.5 Algorithm Noviyanti; Ade Irma Purnamasari; Agus Bahtiar; Edi Tohidi
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 4 No. 3 (2025): June 2025
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v4i3.729

Abstract

  Payment of tuition fees is one of the important aspects of school financial management. At SMK Istiqomah Maruyung, the management of SPP payments is still done manually, which causes student non-compliance in paying on time. The purpose of the research is to improve the SPP payment model by using the C4.5 algorithm to classify the level of student compliance and identify the main factors that influence late payments. The method used is the Knowledge Discovery in Databases (KDD) approach which includes the stages of data selection, preprocessing, transformation, data mining, and result evaluation. The research data was taken from 206 students in the 2023/2024 academic year with attributes such as parental income, number of siblings, scholarship status, and academic grade point average. The C4.5 algorithm was applied to build a decision tree model, with evaluation using five-fold cross validation. The result of this study is that the C4.5 algorithm is able to classify student compliance levels with an average accuracy of 93.55%. The main factors that influence late payment are academic grade point average, class, and parental income. Although the model is very good at predicting compliant students (precision 95%, recall 98%), it shows weakness in predicting lateness (precision 67%, recall 40%). It is concluded that the C4.5 algorithm can improve the efficiency of managing tuition payments and provide data-driven insights for policy making. With further implementation, this algorithm is expected to be adopted by other educational institutions to address similar challenges in financial management.
Sentiment analysis to classify TikTok Shop Users on Twitter with Naïve Bayes Classifier Algorithm Ayu Lestari; Ade Irma Purnamasari; Agus Bahtiar; Edi Tohidi
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 4 No. 2 (2025): February 2025
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v4i2.748

Abstract

Advances in information technology have facilitated the use of social media as an e-commerce platform, with TikTok Shop enabling in-person transactions. This research addresses the gap in understanding user perceptions of TikTok Shop through sentiment analysis on Twitter. Sentiment classification is performed using the Naïve Bayes Classifier algorithm. The dataset consists of 1,907 Indonesian tweets, collected from January 2023 to July 2024, and processed using RapidMiner in the Knowledge Discovery in Database (KDD) framework. The preprocessing stages include data cleaning, normalization, tokenization, stopword removal, and stemming. To overcome data imbalance, Synthetic Minority Oversampling Technique (SMOTE) was applied. The model achieved 93.98% accuracy, with balanced precision and recall for positive, neutral, and negative sentiments. The sentiment distribution among TikTok Shop users on Twitter was 35.5% positive, 35.5% negative, and 29.0% neutral. This research provides insights into consumer behavior on social media and emphasizes the importance of sentiment analysis to increase user engagement and understand market perception. This research is expected to provide information to platform developers and businesses looking to improve TikTok
K-Means Algorithm for Grouping Models of Dengue Fever Prone Areas in Cirebon City Aida Safitri; Ade Irma Purnamasari; Agus Bahtiar; Edi Tohidi
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 4 No. 2 (2025): February 2025
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v4i2.834

Abstract

Dengue hemorrhagic fever (DHF) is an infectious disease transmitted through the Aedes aegypti mosquito. DHF cases in Cirebon City show a significant increase every year. This study aims to classify dengue prone areas based on case data per health center in 2020-2024 obtained from the Cirebon City Health Office. The method used is the K-Means algorithm with the Knowledge Discovery in Database (KDD) approach, which includes data selection, preprocessing, data transformation, data mining, evaluation, and knowledge. Evaluation using Davies-Bouldin Index (DBI) showed optimal results at k = 6 with a DBI value of -0.445. The clustering results produced six clusters: cluster 5 (437 dengue cases in 34 health centers) showed high risk; cluster 0 (244 cases), cluster 2 (129 cases), and cluster 3 (279 cases) showed medium risk; while cluster 1 (69 cases) and cluster 4 (86 cases) showed low risk. This study shows that the K-Means algorithm is effective in identifying DHF risk distribution patterns and provides a strategic basis for the Cirebon City Health Office to prioritize interventions and develop more effective prevention strategies.
K-Means Algorithm to Improve Leaf Image Clustering Model for Rice Disease Early Detection Gina Regiana; Ade Irma Purnamasari; Agus Bahtiar; Edi Tohidi
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 4 No. 2 (2025): February 2025
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v4i2.840

Abstract

This research aims to improve the accuracy of rice leaf image clustering in early disease detection using the K-Means algorithm. The approach used involves the Knowledge Discovery in Databases (KDD) method, which includes data selection, pre-processing, data transformation, data mining, evaluation, and presentation of results. The dataset used consists of images of healthy leaves and leaves infected with diseases such as Bacterial Leaf Blight, Brown Spot, and Leaf Smut. The images are processed through grayscale conversion, noise removal, size adjustment, and data augmentation. The K-Means algorithm is applied to cluster image features based on visual similarity. Evaluation results using Silhouette Score showed that the best clustering was obtained at K=2 with a score of 0.8340, resulting in two main clusters separating healthy and infected images. This study concludes that the K-Means algorithm is able to improve the efficiency and accuracy of rice disease detection, so that it can assist farmers in taking early preventive measures and increase agricultural productivity. This implementation shows significant potential in the development of smart agriculture technology.
Co-Authors Ade Rizki Rinaldi Adisty Tri Putra Aditiya Arif Firmansyah Ahmad Faqih Aida Safitri Akhyanto Akhyanto Aldiyansyah Kurniawan Andi Setiawan Andri Yanto Ayu Lestari Bani Nurhakim Cep Lukman Rohmat Danya Rizki Chaerunisa Denni Pratama Dimas Ardiansyah Dina Audina Edi Tohidi Edi Wahyudin Fadhil Muhammad Basysyar Fathrurrahman Fathurrohman Fathurrohman Febri Abdi Annur Dhuha Gifthera Dwilestari Gina Regiana Habil Jabbal Firdausyi Hadit Attaufiqqurrohman Indra Wiguna Marthanu Intan Wangi Nur Qibti Irfan Ali Kaslani Kurniawan Fajar Abdulloh Muhammad Abib Allesdio Nana Suarna Nana Suarna Nanda Permatasari Neni Lusianah Nining Rahaningsih Noviyanti Odi Nurdiawan Puji Pramudya Marta Putri Trisnawati Raditya Danar Dana Rio Febriyan Salsa Billa Agistina Tati Suprapti Wahyu Saputra Willy Prihartono Yudhistira Arie Wijaya