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INDONESIA
Jurnal Algoritma
Published by Institut Teknologi Garut
ISSN : 14123622     EISSN : 23027339     DOI : https://doi.org/10.33364/algoritma
Core Subject : Science,
Computer Science & IT
Jurnal Algoritma merupakan jurnal yang digunakan untuk mempublikasikan hasil penelitian dalam bidang Teknologi Informasi (TI), Sistem Informasi (SI), dan Rekayasa Perangkat Lunak (RPL), Multimedia (MM), dan Ilmu Komputer (Computer Science).
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 1,145 Documents
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Penerapan Data Mining Untuk Prediksi Kelulusan Siswa Sekolah Dasar Menggunakan Algoritma Naïve Bayes Classifier Aurike Wijaya; Anita; Marchelina Chistina Manurung; Yosef Dwi Santosa Sitanggang
Jurnal Algoritma Vol 23 No 1 (2026): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.23-1.3448

Abstract

Government regulations that heavily influence graduation decisions often lead to data imbalances that obscure the effectiveness of machine learning models in the education sector. This study evaluates the performance of the Naïve Bayes algorithm and compares it with Decision Tree and K-NN on a dataset of 385 students from SD Negeri 067053 Medan Deli, which exhibits extreme label imbalance (with the “Pass” class dominating at 88%). Model evaluation was conducted using Stratified 10-Fold Cross Validation. Test results show that Naïve Bayes achieved a high accuracy of 94.04% and proved to be the most robust in identifying the minority class with a Recall of 91.11%, outperforming other comparison algorithms that suffered from overfitting. However, this high accuracy masked an administrative bias, where the precision of Naïve Bayes in predicting the “Fail” class plummeted to 68.33%. This study confirms that accuracy metrics alone can be misleading on imbalanced data, making the application of resampling techniques during the data preprocessing stage absolutely necessary to address bias in educational data mining implementations.
Pengembangan Sistem Online Public Access Catalog Perpustakaan dengan Fitur Tracking Minat Baca Kordinal Depriansyah; Angga Bayu
Jurnal Algoritma Vol 23 No 1 (2026): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.23-1.3449

Abstract

School library digitalization is generally limited to operational circulation aspects without exploring user behavior data. At SMA IT Quran Qordhova, the conventional system hindered search efficiency and made it difficult to map students’ reading interests. This study aims to develop a web-based Online Public Access Catalog (OPAC) system integrated with a user activity tracking feature. The system was developed using the CodeIgniter 4 framework with the Waterfall methodology. Black Box testing results on 80 functional scenarios showed a 100 percent success rate in interface functionality execution. The main contribution of this study lies in the automatic recording of query logs and click interactions, which are visualized through a descriptive analytics dashboard, as well as the integration of the WhatsApp API to improve the efficiency of circulation administration processes. This system provides an initial empirical foundation for school administrators in formulating library collection procurement policies that are adaptive to students’ reading interests.
Segmentasi Gudang E-Commerce Berdasarkan Biaya Logistik dan Pola Transaksi Menggunakan Metode K-Means dan Fuzzy C-Means Saidina Ali Habib; Aditia Yudhistira
Jurnal Algoritma Vol 23 No 1 (2026): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.23-1.3457

Abstract

Sistem distribusi multi-gudang e-commerce menghadapi tantangan segmentasi data logistik akibat karakteristik transaksi yang heterogen, tidak seimbang, dan memiliki overlap antarvariabel yang tinggi. Penelitian ini bertujuan melakukan segmentasi gudang berdasarkan biaya logistik dan pola transaksi menggunakan algoritma K-Means dan Fuzzy C-Means. Penelitian mengintegrasikan feature engineering berbasis rasio melalui Logistics Cost Ratio (LCR), Value Density (VD), dan Transaction Intensity (TI), serta menerapkan robust scaling dan outlier trimming sebesar 1% untuk meningkatkan stabilitas clustering. Dataset terdiri dari 13.550 transaksi dari empat gudang utama dengan 13.284 data valid setelah preprocessing. Evaluasi dilakukan menggunakan Silhouette Score, Davies-Bouldin Index (DBI), dan Calinski-Harabasz Index (CHI). Hasil penelitian menunjukkan bahwa K-Means dengan k = 3 menghasilkan performa terbaik dengan Silhouette Score sebesar 0,421, DBI sebesar 0,584, dan CHI sebesar 12.273,98. Transformasi fitur berbasis rasio terbukti menghasilkan distribusi cluster yang lebih seimbang dan interpretatif dibandingkan penggunaan data mentah. Segmentasi yang dihasilkan terdiri atas cluster efisiensi tinggi, cluster transaksi reguler, dan cluster beban logistik tinggi yang dapat digunakan sebagai dasar pengambilan keputusan distribusi logistik berbasis data pada sistem multi-gudang e-commerce.
Evaluasi Model BERT Untuk Intent Recognition Pada Chatbot Edukasi Etika Penggunaan AI Dalam Lingkungan Akademik Ayunda Kusuma Wardani; Aris Tjahyanto
Jurnal Algoritma Vol 23 No 1 (2026): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.23-1.3463

Abstract

The development of Artificial Intelligence (AI) technology in education provides convenience in the learning process, but also creates challenges related to understanding the ethics of AI use in academic environments. This study aims to evaluate the performance of several BERT model variants in performing intent recognition for an educational chatbot on the ethical use of AI, particularly in higher education environments. The models used include IndoBERT, Multilingual BERT, and DistilBERT. The dataset consists of 700 data points with 7 intent categories developed using a Retrieval-Augmented Generation (RAG) approach based on the 2024 guideline book on the use of Generative AI from the Ministry of Education, Culture, Research, and Technology. The models were evaluated using accuracy, precision, recall, and F1-score metrics, while also handling out-of-scope (OOS) questions by comparing confidence threshold and entropy-based detection methods. The results show that IndoBERT achieved the best performance, with accuracy, precision, recall, and F1-score values of 97 percent, outperforming Multilingual BERT and DistilBERT. In addition, the entropy-based detection method achieved an accuracy of 95 percent and performed better in detecting out-of-scope questions compared to the confidence threshold method. These findings indicate that IndoBERT is an effective model for intent recognition in an educational chatbot on the ethical use of AI in academic environments.
Pengembangan Sistem Informasi Logistik Berbasis Web Dengan Fitur BAST Menggunakan Metode Waterfall Hamid Abdul Rozak; Giat Karyono
Jurnal Algoritma Vol 23 No 1 (2026): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.23-1.3468

Abstract

Sistem informasi logistik di Universitas Amikom Purwokerto belum menyediakan fitur Berita Acara Serah Terima (BAST) secara digital. Penelitian ini mengembangkan modul BAST yang terintegrasi ke dalam sistem logistik berbasis web menggunakan framework Laravel dan MySQL melalui metode Waterfall. Fitur BAST dirancang terintegrasi dengan data transaksi barang melalui mekanisme checklist transaksional sehingga pengguna dapat menyusun, menyimpan, dan mencetak dokumen tanpa input ulang data secara manual. Pengujian dilakukan melalui Black Box Testing dengan 7 skenario pengujian dan User Acceptance Testing (UAT) yang melibatkan 2 pengguna, mencakup aspek fungsionalitas, kemudahan penggunaan, dan efisiensi pencetakan dokumen. Seluruh skenario pengujian berhasil dijalankan sesuai kebutuhan dan sistem mendapat penilaian baik hingga sangat baik pada seluruh aspek UAT, menunjukkan bahwa integrasi fitur BAST mampu meningkatkan efisiensi administrasi logistik secara signifikan. Penelitian selanjutnya disarankan menggunakan metode pengukuran usability yang lebih terstandar seperti System Usability Scale (SUS).
Prediksi Harga Cryptocurrency Multi-Aset Menggunakan Machine Learning dan Deep Learning Yusuf Nur Alam; Berlilana
Jurnal Algoritma Vol 23 No 1 (2026): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.23-1.3473

Abstract

Cryptocurrency price volatility requires predictive models capable of accurately capturing non-linear patterns. This study predicts the price of Bitcoin (BTCUSDT) as the main asset, as well as Ethereum (ETHUSDT) and Ripple (XRPUSDT) as comparison assets, using Decision Tree, Random Forest, XGBoost, and LSTM models. The novelty of this study lies in the analysis of temporal data leakage and the evaluation of model extrapolation capability within a uniform experimental framework. Daily historical data were processed through cleaning, correlation analysis, variable selection, and sequential 70:30 data splitting. The prediction target was defined as the next-day closing price to avoid data leakage, and the models were evaluated using time-series cross-validation with RMSE, MAPE, and R² metrics. The results show that the best-performing model differs for each asset: LSTM outperformed other models for BTC and XRP, while Random Forest performed best for ETH, with R² values ranging from 0.60 to 0.98. Tree-based models tended to produce flat predictions when test prices exceeded the training data range. These findings emphasize the importance of defining prediction targets, applying temporal validation, and conducting cross-asset evaluation in selecting appropriate models for cryptocurrency price prediction.
Penerapan Blockchain untuk Verifikasi Sertifikat Akademik di Pendidikan Tinggi: Tinjauan Sistematis Julian Lirama Junior Pandari; Krismiyati; Theophilus Wellem
Jurnal Algoritma Vol 23 No 1 (2026): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.23-1.3475

Abstract

Transformasi digital di pendidikan tinggi memerlukan verifikasi sertifikat akademik yang aman dan efisien untuk mengatasi keterbatasan sistem konvensional, seperti risiko pemalsuan dan lambat pemrosesan. Teknologi blockchain menawarkan solusi pencatatan data terdistribusi yang transparan, namun penerapannya di negara berkembang masih terkendala infrastruktur dan regulasi. Penelitian ini menggunakan metode Systematic Literature Review (SLR) dengan panduan PRISMA. Pencarian literatur dilakukan pada basis data Scopus, IEEE Xplore, Google Scholar, ACM Digital Library, dan SpringerLink untuk mengeksplorasi publikasi periode 2017-2024. Dari semua artikel yang diidentifikasi, 35 studi dipilih melalui proses penyaringan dan penilaian kualitas yang dilakukan secara naratif kemudian disintesis menggunakan pendekatan tematik. Penelitian ini menghasilkan sintesis komprehensif mengenai evolusi penerapan blockchain dalam verifikasi sertifikat akademik serta mengidentifikasi model blockchain yang mengintegrasikan Hyperledger Fabric, Verifiable Credentials dan InterPlanetary File System sebagai pendekatan arsitektur hybrid. Model yang diusulkan ini bukan dipandang sebagai pengganti sistem nasional yang sudah ada melainkan sebagai lapisan kepercayaan tambahan yang melengkapinya. Pendekatan ini dinilai mampu menyeimbangkan keamanan data, skalabilitas, serta efisiensi sumber daya di negara berkembang, sekaligus mengidentifikasi kesenjangan penelitian untuk validasi empiris lebih lanjut. Keterbatasan penelitian ini terletak pada sifatnya yang tinjauan literatur sehinggah temuannya masih bersifat konseptual.
Analisis Spasio-Temporal Traffic Pattern via Interactive Dashboard: Rancang Bangun Decision Support System demi Eskalasi Situational Awareness Keselamatan Maritim di VTS Merak Mohammad Darsoni; Deddy Pratama; Wandi Febriansyah
Jurnal Algoritma Vol 23 No 1 (2026): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.23-1.3478

Abstract

The Sunda Strait is a strategic shipping lane with a high density of vessel traffic, which increases the potential risk of maritime accidents. Therefore, analyzing traffic density patterns is essential to support safer and more efficient navigation management. This study aims to identify vessel traffic density patterns using machine learning-based clustering techniques, as well as to compare the performance of the K-Means and DBSCAN algorithms. The data used are secondary data obtained from VTS Merak, consisting of daily vessel counts categorized based on Traffic Separation Scheme (TSS) routes, namely Passing North, Passing South, Crossing West, Crossing East, and Vessel Not Using TSS. The data were processed through preprocessing stages, including data cleaning and normalization using the Min-Max Scaling method. The analysis was conducted by applying K-Means and DBSCAN algorithms and evaluated using the Silhouette Score and Davies-Bouldin Index. The results indicate that DBSCAN is more effective in identifying complex density patterns, forming density-based clusters, and detecting anomalies in vessel trajectories. In contrast, K-Means produces more structured clusters but is less flexible in handling irregular data patterns. Therefore, DBSCAN is considered superior for analyzing vessel traffic density in the Sunda Strait and has strong potential to support decision-making in improving maritime safety.
Klasifikasi Sentimen Ulasan GoFood di Google Play Store dengan Metode Naive Bayes Dwi Diva Teresia Situngkir; Anita; Dheo Laurenz Purba
Jurnal Algoritma Vol 23 No 1 (2026): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.23-1.3479

Abstract

GoFood is a food delivery feature within the Gojek application that has received numerous user reviews on the Google Play Store. The high volume of reviews creates a need for efficient automated sentiment analysis. This study aims to classify the sentiment of GoFood reviews using the Multinomial Naive Bayes method with TF-IDF weighting. A total of 1,649 Indonesian-language reviews were collected through web scraping from the Google Play Store, then processed through preprocessing and sentiment labeling stages, with an 80 percent training data and 20 percent testing data split. The evaluation results show an accuracy of 78.14 percent, with negative sentiment precision of 0.76 and recall of 1.00, as well as positive sentiment precision of 0.96. The low positive recall was caused by data imbalance and the absence of data balancing techniques such as SMOTE. The scientific contribution of this study is the provision of a sentiment map based on Multinomial Naive Bayes and TF-IDF as a reference for GoFood service evaluation and the development of Indonesian-language text sentiment analysis.
K-Means Clustering Menghasilkan Tiga Segmen Layanan Sertifikasi Kapal Berbasis Karakteristik Operasional Hermina; Handoyo Widi Nugroho
Jurnal Algoritma Vol 23 No 1 (2026): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.23-1.3486

Abstract

Distribusi beban kerja yang tidak merata pada layanan sertifikasi kapal menjadi tantangan operasional utama instansi pelabuhan. Penelitian ini menerapkan K-Means Clustering pada 18 jenis layanan sertifikasi di KSOP Kelas I Panjang (1.506 sertifikat, Januari–Desember 2025) menggunakan empat variabel operasional: frekuensi pengajuan, waktu penyelesaian, jumlah dokumen, dan kompleksitas layanan. Metode Elbow menghasilkan K = 3 sebagai jumlah cluster optimal. Tiga kelompok terbentuk: Layanan Reguler (6 layanan, kompleksitas rendah), Layanan Kompleks (10 layanan, persyaratan teknis tinggi), dan Layanan Dominan (2 layanan yang menyumbang 62,4 persen total penerbitan). Kualitas cluster dikonfirmasi dengan Silhouette Score 0,6221 dan Davies-Bouldin Index 0,484. Temuan ini berkontribusi secara metodologis dengan mendemonstrasikan bahwa segmentasi berbasis populasi layanan menghasilkan cluster yang valid dan dapat langsung diterapkan sebagai dasar realokasi SDM, prioritas digitalisasi, dan pembentukan tim spesialis di instansi pelabuhan.

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