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Prediksi Kecepatan Angin Menggunakan Gated Recurrent Unit (GRU) dengan Estimasi Ketidakpastian Monte Carlo Dropout pada Data BMKG Tanjung Perak Alvino Hadiyan Pradipta; Muhammad Rafli Feandika Nugroho; Maretta Fairuz Luthfia Winoto Putri; Alfan Rizaldy Pratama; Shindi Shella May Wara; Muhammad Nasrudin
Jurnal Ilmiah ILKOMINFO - Ilmu Komputer & Informatika Vol 9, No 2 (2026): Juli
Publisher : Institut Teknologi Gamalama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47324/ilkominfo.v9i2.491

Abstrak: Keterbatasan metode prediksi konvensional dalam memodelkan dependensi temporal dan ketidakpastian prediksi mendorong pengembangan pendekatan berbasis deep learning. Penelitian ini bertujuan mengembangkan model prediksi kecepatan angin menggunakan metode Gated Recurrent Unit (GRU) pada data meteorologi yang berasal dari stasiun pengamatan BMKG Tanjung Perak. Penelitian ini dilakukan karena metode prediksi sebelumnya masih memiliki keterbatasan dalam menangkap pola temporal dan dependensi jangka panjang pada data time series, serta umumnya belum mengakomodasi ketidakpastian hasil prediksi. GRU dipilih karena mampu memodelkan dependensi temporal secara efisien, sedangkan simulasi Monte Carlo digunakan untuk menghasilkan beberapa skenario prediksi dan mengestimasi interval kepercayaan. Data yang digunakan mencakup parameter kecepatan angin dengan interval waktu tertentu. Hasil evaluasi menunjukkan bahwa model menunjukkan performa yang baik untuk memprediksi kecepatan angin secara akurat, dengan nilai MAE sebesar 0,37, RMSE sebesar 0,50, MAPE sebesar 5,78%, dan R² sebesar 0,986. Dengan demikian, model yang dikembangkan dapat menjadi solusi dalam analisis dan peramalan data time series meteorologi secara komprehensif.Kata kunci: Prediksi Kecepatan Angin, Analisis Time Series, Gated Recurrent Unit (GRU), Simulasi Monte Carlo, Data MeteorologiAbstract: The limitations of conventional forecasting methods in modeling temporal dependencies and forecast uncertainty have driven the development of deep learning-based approaches. This study aims to develop a wind speed forecasting model using the Gated Recurrent Unit (GRU) method on meteorological data from the BMKG Tanjung Perak observation station. This study was conducted because previous prediction methods still have limitations in capturing temporal patterns and long-term dependencies in time series data, and generally do not accommodate the uncertainty of prediction results. GRU was chosen because it is capable of modeling temporal dependencies efficiently, while Monte Carlo simulation was used to generate several prediction scenarios and estimate confidence intervals. The data used includes wind speed parameters at specific time intervals. The evaluation results show that the model shows good performance in predicting wind speed accurately, with an MAE of 0.37, an RMSE of 0.50, a MAPE of 5.78%, and an R² of 0.986. Thus, the developed model can serve as a solution for comprehensive analysis and forecasting of meteorological time series data.Keywords: Wind Speed Prediction, Time Series Analysis, Gated Recurrent Unit (GRU), Monte Carlo Simulation, Meteorological Data

Peran Machine Learning Dalam E-Commerce: Tinjauan Literatur Sistematis Terhadap Penerapan Dan Tantangan Sri Murdiawati; Amri Reza Wahyudin; Juan Adi Putra; Ryan Randy Suryono
Jurnal Ilmiah ILKOMINFO - Ilmu Komputer & Informatika Vol 9, No 2 (2026): Juli
Publisher : Institut Teknologi Gamalama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47324/ilkominfo.v9i2.448

Abstrak: Perkembangan e-commerce menghasilkan banyak data yang besar dan rumit, sehingga membutuhkan teknologi canggih untuk membantu pengambilan keputusan dan meningkatkan pelayanan. Machine learning menjadi cara utama yang digunakan karena kemampuannya untuk mempelajari pola perilaku pengguna dan transaksi secara otomatis. Penelitian ini menganalisis penerapan machine learning dalam e-commerce dengan menggunakan metode Systematic Literature Review (SLR) terhadap 20 artikel jurnal dari dalam dan luar negeri. Kebaruan penelitian ini terletak pada sistesis yang menggabungkan berbagai aspek seperti bidang penerapan, metode algoritma, cara mengukur kinerja, tantangan teknis, serta dampak bisnis dalam satu kerangka analisis yang terstruktur. Hasil penelitian menunjukkan bahwa machine learning memiliki peran penting dalam sistem rekomendasi, analisis sentimen, mendeteksi penipuan, serta memprediksi penjualan, meskipun masih menghadapi tantangan seperti kualitas data, kebutuhan komputasi yang besar, dan kemampuan menjelaskan hasil.Kata Kunci: e-commerce; machine learning; systematic literature review; sistem rekomendasi.Abstract: The development of e-commerce has generated large amounts of complex data, requiring advanced technology to aid decision-making and improve services. Machine learning has become the primary method used due to its ability to automatically learn patterns of user behavior and transactions. This study analyzes the application of machine learning in e-commerce using the Systematic Literature Review (SLR) method on 20 journal articles from within and outside the country. The novelty of this research lies in its synthesis, which combines various aspects such as fields of application, algorithm methods, performance measurement methods, technical challenges, and business impacts into a single structured analytical framework. The results show that machine learning plays an important role in recommendation systems, sentiment analysis, fraud detection, and sales prediction, despite still facing challenges such as data quality, large computational requirements, and the ability to explain results.Keywords: e-commerce; machine learning; systematic literature review; recommendation system

Sentiment Analysis of BPJS Kesehatan Application Reviews Using Optimized XGBoost and Support Vector Machine Muhammad Syafiq; Chandra Kirana; Delpiah Wahyuningsih
Jurnal Ilmiah ILKOMINFO - Ilmu Komputer & Informatika Vol 9, No 2 (2026): Juli
Publisher : Institut Teknologi Gamalama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47324/ilkominfo.v9i2.480

This study addresses a critical gap in automated public service evaluation by systematically comparing the performance of XGBoost and Support Vector Machine (SVM) for sentiment classification of BPJS Kesehatan application reviews. Unlike prior research that predominantly relies on default model configurations or single-algorithm frameworks, this study introduces a rigorously optimized comparative pipeline using GridSearchCV with k-fold cross-validation, specifically designed to address hyperparameter sensitivity and class imbalance in Indonesian digital health feedback. User reviews were extracted from the Google Play Store, preprocessed using a standardized NLP pipeline, and vectorized via TF-IDF. Analytical results reveal that while SVM achieves marginally higher overall accuracy (90.5%) through optimal hyperplane separation, it completely fails to classify neutral sentiments (F1-score = 0.00), highlighting its vulnerability to minority-class underrepresentation. In contrast, XGBoost (89.75% accuracy) demonstrates superior multi-class equilibrium, leveraging ensemble regularization to effectively capture ambiguous and neutral expressions. The systematic integration of GridSearchCV significantly improves generalization, validating hyperparameter optimization as a critical determinant of model robustness in real-world textual data. Scientifically, this study advances methodological understanding by demonstrating the trade-offs between margin-based strictness and ensemble adaptability under exhaustive optimization, providing a reproducible framework for imbalanced sentiment classification. Practically, it offers public health administrators a scalable, data-driven mechanism for real-time service quality monitoring and user satisfaction analytics.Keywords: Sentiment Analysis; XGBoost; Support Vector Machine

Evaluasi Kinerja Algoritma Naive Bayes, Decision Tree, dan Random Forest untuk Prediksi Risiko Default Nasabah Kartu Kreditult Nasabah Kartu Kredit Turlia Indah Sapitri; Tia Dwi Anggra Yani; Jelna Anggreni; Erliyan Redi Susanto
Jurnal Ilmiah ILKOMINFO - Ilmu Komputer & Informatika Vol 9, No 2 (2026): Juli
Publisher : Institut Teknologi Gamalama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47324/ilkominfo.v9i2.449

Abstrak: Prediksi risiko gagal bayar (default) nasabah kartu kredit merupakan aspek penting dalam manajemen risiko kredit pada lembaga keuangan. Berbagai penelitian telah menerapkan algoritma klasifikasi untuk prediksi risiko kredit, namun sebagian besar penelitian hanya berfokus pada satu atau dua algoritma sehingga hasil perbandingan performa antar metode masih terbatas. Penelitian ini bertujuan untuk mengevaluasi dan membandingkan kinerja algoritma Naive Bayes, Decision Tree, dan Random Forest dalam memprediksi risiko default nasabah kartu kredit. Dataset yang digunakan merupakan data sekunder dari platform Kaggle yang terdiri dari 30.000 data nasabah. Tahapan penelitian meliputi preprocessing data, pembagian training dan testing dengan rasio 80:20, pembangunan model klasifikasi, serta evaluasi menggunakan metrik accuracy, precision, recall, dan F1-score. Kontribusi penelitian ini terletak pada evaluasi komparatif tiga algoritma klasifikasi dalam satu kerangka eksperimen yang sama menggunakan pendekatan evaluasi multi-metrik. Hasil penelitian menunjukkan bahwa Random Forest menghasilkan kinerja terbaik berdasarkan nilai accuracy sebesar 0,813500 dan precision sebesar 0,630027. Sementara itu, Naive Bayes memperoleh nilai recall tertinggi sebesar 0,651181 dan F1-score sebesar 0,494363. Temuan penelitian menunjukkan bahwa Random Forest lebih unggul dalam ketepatan klasifikasi, sedangkan Naive Bayes lebih efektif dalam mendeteksi nasabah yang berpotensi mengalami gagal bayar.Kata kunci: Credit Risk Assessment, Kartu Kredit, Prediksi Risiko Kredit, Naive Bayes, Decision Tree, Random ForestAbstract: Credit card default risk prediction is an important aspect of credit risk management in financial institutions. Although various classification algorithms have been applied to credit risk prediction, most previous studies have focused on only one or two algorithms, resulting in limited comparative insights into their performance. This study aims to evaluate and com-pare the performance of Naive Bayes, Decision Tree, and Random Forest algorithms in pre-dicting credit card customer default risk. The dataset used in this study is secondary data ob-tained from Kaggle, consisting of 30,000 customer records. The research process includes data preprocessing, dataset splitting into training and testing sets with an 80:20 ratio, clas-sification model development, and performance evaluation using accuracy, precision, recall, and F1-score metrics. The contribution of this study lies in the comparative evaluation of three classification algorithms within the same experimental framework using a multi-metric evaluation approach. The results indicate that Random Forest achieved the best overall per-formance with an accuracy of 0.813500 and a precision of 0.630027. Meanwhile, Naive Bayes obtained the highest recall of 0.651181 and the highest F1-score of 0.494363. These findings suggest that Random Forest is more effective in overall classification performance, whereas Naive Bayes is better at identifying customers with a higher likelihood of default.Keywords: Credit Risk Assessment, Credit Card, Credit Risk Prediction, Naive Bayes, Decision Tree, Random Forest

Sistem Pendukung Keputusan Evaluasi Kepuasan Peserta Pelatihan E-Commerce Berbasis TOPSIS Kumara Davin Valerian; Wahyu Hadikristanto; Nanang Tedi Kurniadi
Jurnal Ilmiah ILKOMINFO - Ilmu Komputer & Informatika Vol 9, No 2 (2026): Juli
Publisher : Institut Teknologi Gamalama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47324/ilkominfo.v9i2.474

Abstrak: Perkembangan teknologi digital, khususnya e-commerce, menuntut peningkatan kompetensi sumber daya manusia melalui pelatihan yang terarah dan berkualitas. Selain aspek pelaksanaan, keberhasilan pelatihan perlu diukur melalui evaluasi kepuasan peserta secara objektif berdasarkan berbagai kriteria penilaian. Meskipun metode sistem pendukung keputusan telah banyak diterapkan pada berbagai bidang evaluasi, penerapannya pada evaluasi kepuasan peserta pelatihan e-commerce masih relatif terbatas. Penelitian ini bertujuan untuk mengevaluasi kepuasan peserta pelatihan e-commerce menggunakan metode TOPSIS. Kebaruan penelitian ini terletak pada penerapan evaluasi kepuasan berbasis multi-kriteria menggunakan skala likert dan metode TOPSIS pada konteks pelatihan e-commerce, yang belum banyak dibahas pada penelitian sebelumnya. Data diperoleh melalui observasi, studi literatur, dan penyebaran kuesioner kepada 56 responden. Hasil pengolahan menunjukkan nilai preferensi berada pada rentang 0,28 hingga 1,00. Sebanyak 13 alternatif memperoleh nilai preferensi tertinggi sebesar 1,00, sedangkan alternatif ke-51 memperoleh nilai terendah sebesar 0,28. Hasil penelitian menunjukkan bahwa model evaluasi berbasis TOPSIS mampu menghasilkan pemeringkatan kepuasan peserta secara objektif dan terukur, sehingga berkontribusi dalam meningkatkan akurasi pengambilan keputusan pada evaluasi pelatihan e-commerce.Kata kunci: Sistem Pendukung Keputusan, Kepuasan Peserta, Pelatihan E-Commerce, TOPSIS, KuesionerAbstract: The development of digital technology, particularly e-commerce, demands increased human resource competency through targeted and quality training. In addition to implementation aspects, training success needs to be measured through objective participant satisfaction evaluation based on various assessment criteria. Although decision support system methods have been widely applied in various evaluation fields, their application to e-commerce training participant satisfaction evaluation is still relatively limited. This study aims to evaluate e-commerce training participant satisfaction using the TOPSIS method. The novelty of this study lies in the application of multi-criteria-based satisfaction evaluation using a Likert scale and the TOPSIS method in the context of e-commerce training, which has not been widely discussed in previous studies. Data were obtained through observation, literature review, and questionnaire distribution to 56 respondents. The results showed that preference values ranged from 0.28 to 1.00. A total of 13 alternatives obtained the highest preference value of 1.00, while the 51st alternative obtained the lowest value of 0.28. The results show that the TOPSIS-based evaluation model is able to produce objective and measurable participant satisfaction rankings, thus contributing to improving the accuracy of decision-making in e-commerce training evaluation.Keywords: Decision Support System, Participant Satisfaction, E-Commerce Training, TOPSIS, Questionnaire

XGBoost-Based Sentiment Analysis for Evaluating Customer Satisfaction at Hotel Puri Ansel Thalia Puspita Sari; Chandra Kirana; Delpiah Wahyuningsih
Jurnal Ilmiah ILKOMINFO - Ilmu Komputer & Informatika Vol 9, No 2 (2026): Juli
Publisher : Institut Teknologi Gamalama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47324/ilkominfo.v9i2.484

While sentiment analysis is increasingly applied in hospitality research, existing studies predominantly rely on large, balanced datasets from global platforms or computationally intensive deep learning models that lack interpretability for local hotel management. A critical research gap remains in deploying lightweight, interpretable machine learning frameworks on small, highly imbalanced Indonesian hotel reviews while translating sentiment outputs into actionable operational insights. To address this gap, this study evaluates customer satisfaction at Hotel Puri Ansel using an XGBoost-based sentiment classification pipeline optimized for real-world data constraints. Google Reviews were processed through comprehensive Indonesian text preprocessing and TF-IDF feature extraction, then partitioned into 80% training and 20% testing sets. The XGBoost model achieved 84% accuracy and a 0.83 weighted F1-score, demonstrating exceptional positive sentiment recall (97%). Lexical analysis identified “cleanliness” and “comfort” as primary satisfaction drivers, whereas “hygiene issues” and “slow service” dominated negative feedback. Although the model exhibited limitations with ambiguous and sarcastic expressions, its novelty lies in bridging technical classification performance with interpretable business intelligence. This study contributes a reproducible, resource-efficient framework that enables local hospitality operators to leverage unstructured review data for targeted service improvements, prioritizing practical deployment validity over artificial data balancing.Keywords: Sentiment Analysis, XGBoost, Hotel Service Quality

Pembangunan Game Edukasi Numerasi Berbasis Android Menggunakan Algoritma Fisher-Yates Shuffle Fahmi Abdullah; Renzi Thalia; Aisya Khotimatul Aula
Jurnal Ilmiah ILKOMINFO - Ilmu Komputer & Informatika Vol 9, No 2 (2026): Juli
Publisher : Institut Teknologi Gamalama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47324/ilkominfo.v9i2.475

Abstrak: Kemampuan numerasi menjadi salah satu kompetensi dasar yang dibutuhkan siswa dalam menghadapi kehidupan sehari-hari. Namun, berdasarkan Programme for International Student Assessment (PISA) 2022, kemampuan numerasi siswa di Indonesia masih rendah. Kondisi serupa terjadi di SDN Toblong 2, di mana pembelajaran masih menggunakan metode konvensional dengan soal yang monoton sehingga menurunkan motivasi belajar siswa. Penelitian ini bertujuan menghadirkan solusi berbasis teknologi berupa media pembelajaran ke dalam game edukasi berbasis Android menggunakan algoritma Fisher-Yates Shuffle untuk menghasilkan variasi soal secara acak sekaligus meningkatkan keterlibatan siswa melalui gamifikasi. Metode yang digunakan adalah Game Development Life Cycle (GDLC) dengan materi bilangan, aljabar, geometri, dan pengukuran untuk siswa kelas 5. Pengujian dilakukan melalui kuesioner dan wawancara. Hasil menunjukkan aplikasi efektif membantu pemahaman siswa dengan tingkat persetujuan 89,06%, meningkatkan tantangan belajar sebesar 87,33%, serta meningkatkan motivasi siswa sebesar 90,00%. Berdasarkan hasil tersebut bahwa game edukasi menggunakan algoritma shuffle menjadi media pembelajaran alternatif yang lebih efektif, tidak mudah bosan, dan memotivasi siswa.Kata kunci: Game Edukasi, Numerasi, Android, Algoritma Fisher Yates Shuffle, Gamifikasi.Abstract: Numeracy skills are one of the core competencies students need to navigate daily life. However, according to the Programme for International Student Assessment (PISA) 2022, students’ numeracy skills in Indonesia remain low. A similar situation exists at SDN Toblong 2, where instruction still relies on conventional methods with monotonous problems, thereby reducing students’ motivation to learn. This study aims to provide a technology-based solution in the form of educational content integrated into an Android-based educational game using the Fisher-Yates Shuffle algorithm to generate random variations of questions while enhancing student engagement through gamification. The method used is the Game Development Life Cycle (GDLC) with content covering numbers, algebra, geometry, and measurement for 5th-grade students. Testing was conducted via questionnaires and interviews. The results show that the application effectively aids student understanding with an approval rate of 89.06%, increases the challenge of learning by 87.33%, and boosts student motivation by 90.00%. Based on these results, educational games using the shuffle algorithm serve as a more effective alternative learning medium that is less likely to cause boredom and motivates students.Keywords: Educational Games, Numeracy, Android, Fisher-Yates Shuffle Algorithm, Gamification

Prediksi Risiko Penyakit Stroke Menggunakan Logistic Regression Dengan Dashboard Interaktif Aldillah Aldillah; Khairunnas .; Irma Eryanti Putri
Jurnal Ilmiah ILKOMINFO - Ilmu Komputer & Informatika Vol 9, No 2 (2026): Juli
Publisher : Institut Teknologi Gamalama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47324/ilkominfo.v9i2.485

Abstrak: Stroke merupakan salah satu penyebab utama kematian dan kecacatan jangka panjang di dunia. Peningkatan kasus stroke mendorong pemanfaatan teknologi machine learning untuk membantu prediksi risiko penyakit secara cepat dan akurat. Penelitian sebelumnya telah menggunakan algoritma seperti Support Vector Machine dan Random Forest, namun sebagian besar masih berfokus pada performa model dan belum mengintegrasikan hasil prediksi dalam media visual yang interaktif. Penelitian ini bertujuan membangun model prediksi risiko stroke menggunakan algoritma Logistic Regression serta mengimplementasikannya dalam dashboard interaktif. Dataset yang digunakan berasal dari Kaggle dengan jumlah data sebanyak 5110. Tahapan penelitian meliputi preprocessing data, pembagian data latih 80% dan data uji 20%, serta pelatihan model menggunakan Logistic Regression. Hasil penelitian menunjukkan model memperoleh akurasi sebesar 92% dan ROC-AUC sebesar 84%. Dashboard interaktif yang dikembangkan mampu menampilkan probabilitas risiko stroke dan variabel yang berpengaruh sehingga membantu pengguna memahami hasil prediksi secara lebih efektif.Kata kunci: Data Mining; Logistic Regression; StrokeAbstract: Stroke is one of the leading causes of death and long-term disability worldwide. The rise in stroke cases has driven the use of machine learning technology to help predict disease risk quickly and accurately. Previous research has employed algorithms such as Support Vector Machines and Random Forests; however, most studies have focused primarily on model performance and have not integrated prediction results into interactive visual media. This study aims to build a stroke risk prediction model using the Logistic Regression algorithm and implement it in an interactive dashboard. The dataset used comes from Kaggle, containing 5,110 data points. The research stages include data preprocessing, splitting the data into an 80% training set and a 20% test set, and training the model using Logistic Regression. The results show that the model achieved an accuracy of 92% and an ROC-AUC of 84%. The developed interactive dashboard displays stroke risk probabilities and influencing variables, helping users understand the prediction results more effectively..Keywords: Data Mining, Logistic Regression, Stroke

Optimasi Proses Pembayaran dan Pencatatan SPP Berbasis Digital dengan Pendekatan Business Process Reengineering dan BPMN di SMK Ngunut Amalia Ramadhani Putri; Wildan Suharso
Jurnal Ilmiah ILKOMINFO - Ilmu Komputer & Informatika Vol 9, No 2 (2026): Juli
Publisher : Institut Teknologi Gamalama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47324/ilkominfo.v9i2.476

Abstrak: Penelitian ini bertujuan untuk meningkatkan efisiensi proses pembayaran dan pencatatan Sumbangan Pembinaan Pendidikan (SPP/PRM) di SMK Ngunut Tulungagung yang masih dilakukan secara manual. Keterbaruan penelitian ini terletak pada integrasi pendekatan Business Process Reengineering (BPR), Business Process Model and Notation (BPMN), analisis efisiensi throughput, serta usability testing dalam pengembangan sistem pembayaran digital berbasis web (e-SPP) di lingkungan SMK. Data diperoleh melalui observasi dan wawancara dengan bendahara sekolah serta petugas loket. Hasil analisis menunjukkan efisiensi throughput awal pada proses pembayaran sebesar 50% dan pencatatan sebesar 85%. Setelah dilakukan perancangan ulang berbasis sistem digital terintegrasi, proses pembayaran dan pencatatan dapat dilakukan secara otomatis dan real-time, sehingga menghilangkan antrean, pencatatan berulang, dan rekapitulasi manual. Implementasi sistem meningkatkan efisiensi throughput menjadi 100% serta menurunkan waktu proses dari 216 menit menjadi 12 menit. Hasil usability testing menggunakan metode System Usability Scale (SUS) menunjukkan kategori Good dan Acceptable, yang menandakan sistem dapat diterima pengguna. Penelitian ini berkontribusi terhadap pengembangan sistem informasi pendidikan dalam mendukung transformasi digital administrasi keuangan sekolah.Kata kunci: Business Process Reengineering, BPMN, Pembayaran SPP, Sistem Informasi Pendidikan, Efisiensi ThroughputAbstract: This study aims to improve the efficiency of tuition payment and recording processes (SPP/PRM) at SMK Ngunut Tulungagung, which are still conducted manually. The novelty of this study lies in the integration of Business Process Reengineering (BPR), Business Process Model and Notation (BPMN), throughput efficiency analysis, and usability testing in the development of a web-based digital payment system (e-SPP) in the vocational school environment. Data were collected through observations and interviews with the school treasurer and payment counter staff. The analysis results indicate that the initial throughput efficiency was 50% for the payment process and 85% for the recording process. After implementing a redesigned integrated digital system, payment and recording activities could be performed automatically and in real-time, eliminating queues, redundant records, and manual recapitulation processes. The implementation increased throughput efficiency to 100% and reduced processing time from 216 minutes to 12 minutes. Furthermore, usability testing using the System Usability Scale (SUS) resulted in Good and Acceptable categories, indicating that the proposed system is well accepted by users. This study contributes to the development of educational information systems in supporting the digital transformation of school financial administration.Keywords: Business Process Reengineering, BPMN, Tuition Payment, Educational Information System, Throughput Efficiency

Sistem Informasi Geografis Potensi Desa Berbasis Web Menggunakan Algoritma Dijkstra Tarsinah Sumarni; Riski Maulana; Brian Damastu Ridho Hutama
Jurnal Ilmiah ILKOMINFO - Ilmu Komputer & Informatika Vol 9, No 2 (2026): Juli
Publisher : Institut Teknologi Gamalama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47324/ilkominfo.v9i2.482

Abstrak: Penelitian ini memiliki kebaruan berupa penerapan algoritma Dijkstra pada Sistem Informasi Geografis (SIG) potensi desa berbasis web, yang masih jarang diterapkan pada visualisasi potensi pedesaan. Sistem dikembangkan untuk memetakan potensi Desa Karangjaladri secara akurat dan interaktif menggunakan metode Rapid Application Development (RAD). Algoritma Dijkstra digunakan untuk menentukan rute terpendek menuju lokasi potensi desa pada sektor UMKM, pariwisata, perikanan, dan pertanian. Sistem dibangun menggunakan PHP, MySQL, dan OpenStreetMap guna mendukung pengelolaan data spasial dan penyebaran informasi desa secara lebih efektif. Selain itu, sistem diharapkan dapat membantu masyarakat dan perangkat desa dalam mendukung perencanaan pembangunan yang transparan dan partisipatif. Hasil pengujian beta menggunakan Technology Acceptance Model (TAM) terhadap 134 responden menunjukkan respons sangat positif, dengan nilai Perceived Ease of Use 88,8%, Perceived Usefulness 89,55%, Attitude Toward Using 89,698%, Behavioral Intention 90,07%, dan Actual Use 89,50%.Kata kunci: Sistem Informasi Geografis, Potensi Desa, Algoritma Dijkstra, Rapid Application Development (RAD)Abstract: This research offers novelty through the implementation of the Dijkstra algorithm in a web-based Geographic Information System (GIS) for village potential mapping, which is still rarely applied in rural potential visualization. The system was developed to map the potential of Karangjaladri Village accurately and interactively using the Rapid Application Development (RAD) method. The Dijkstra algorithm was applied to determine the shortest routes to village potential locations in the sectors of MSMEs, tourism, fisheries, and agriculture. The system was built using PHP, MySQL, and OpenStreetMap to support spatial data management and information dissemination more effectively. In addition, the system is expected to assist the community and village officials in supporting transparent and participatory development planning. The beta testing results using the Technology Acceptance Model (TAM) involving 134 respondents showed very positive responses, with scores of 88.8% for Perceived Ease of Use, 89.55% for Perceived Usefulness, 89.698% for Attitude Toward Using, 90.07% for Behavioral Intention, and 89.50% for Actual Use.Keywords: Geographic Information System, Village Potential, Dijkstra Algorithm, Rapid Application Development (RAD)

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