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Progresif: Jurnal Ilmiah Komputer
Published by Sekolah Tinggi Manajemen Informatika dan Komputer Banjarbaru
ISSN : 02163284     EISSN : 26850877     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering
Progresif: Jurnal Ilmiah Komputer adalah Jurnal Ilmiah bidang Komputer yang diterbitkan secara periodik dua nomor dalam satu tahun, yaitu pada bulan Februari dan Agustus. Redaksi Progresif: Jurnal Ilmiah Komputer menerima Artikel hasil penelitian atau atau artikel konseptual bidang Komputer.
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Articles 542 Documents
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Phishing URL Detection Using Hybrid CNN-BiLSTM Character-Level Deep Learning Alrafiful Rahman; Pratiwi Rachmadi; Farah Kaylila; Sae Khatami; Maria Stefani
Progresif: Jurnal Ilmiah Komputer Vol 22, No 3 (2026): Juli
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/progresif.v22i3.3753

Abstract

Phishing attacks conducted through fraudulent URLs continue to become a significant cybersecurity challenge, demanding detection methods that are both rapid and reliable. This research introduces a hybrid deep learning model that integrates Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) architectures for automated phishing URL detection. The CNN component is utilized to identify lexical and character-level patterns within URLs, whereas BiLSTM captures contextual dependencies from both forward and backward sequence directions. The proposed model was trained using 450,176 labeled URL samples with character-level tokenization, minimizing the need for manual feature extraction. Experimental evaluations demonstrated excellent performance, achieving an accuracy of 99.78%, while precision and recall consistently remained above 99% according to confusion matrix analysis. Furthermore, the trained model was implemented into a Python-based check_link() function that integrates deep learning prediction scores with rule-based analysis to categorize URLs into safe, suspicious, or dangerous classes. Nevertheless, inference testing revealed that the model is sensitive to incomplete URL formats, particularly legitimate domains lacking the www prefix. In general, the CNN-BiLSTM approach proved highly effective for large-scale phishing URL classification, although incorporating additional external features may help reduce false positive predictions.Keywords: Phising; URL; Deep learning; Convolutional Neural Network; Bidirectional Long Short-Term Memory.
Analisis Kinerja Algoritma Naïve Bayes Dalam Prediksi Tingkat Persediaan Susu Formula Pada Sektor Ritel Muhammad Erwan Fuqoha Suryanata; Budi Rahmani
Progresif: Jurnal Ilmiah Komputer Vol 22, No 3 (2026): Juli
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/progresif.v22i3.3946

Abstract

Formula milk inventory management at Toko Serba Ada has traditionally relied on a manual system that is often inefficient and error-prone. One of the main challenges is the mismatch between inventory levels and market demand, leading to overstocking and the risk of financial losses from product expiration. This study aims to analyze the performance of the Naïve Bayes algorithm in predicting formula milk inventory levels in the retail sector. The variables used in this study include initial stock, demand trends, product price, and sales volume. The prediction model was developed as a web-based application using PHP. Model performance was evaluated using a confusion matrix with accuracy, precision, and recall as evaluation metrics. The experimental results showed that the model achieved 71.43% accuracy, 73.76% precision, and 74.08% recall. These findings indicate that the Naïve Bayes algorithm demonstrates satisfactory decision-making capability for formula milk inventory management in the retail sector.Keywords: Naïve Bayes; Inventory Prediction; Formula Milk; Retail Sector; Inventory Management. AbstrakSelama ini, pengelolaan stok susu formula di Toko Serba Ada masih mengandalkan sistem manual yang sering kali kurang efisien dan rentan terhadap kesalahan manusia. Masalah utama yang sering muncul adalah ketidaksesuaian antara jumlah stok dan permintaan pasar sehingga menyebabkan penumpukan produk maupun risiko kerugian akibat kedaluwarsa. Penelitian ini bertujuan untuk menganalisis kinerja algoritma Naïve Bayes dalam memprediksi tingkat persediaan susu formula pada sektor ritel. Variabel yang digunakan meliputi stok awal, tren permintaan, harga, dan tingkat penjualan. Model dikembangkan dalam aplikasi berbasis web menggunakan bahasa pemrograman PHP. Evaluasi dilakukan menggunakan confusion matrix dengan parameter akurasi (accuracy), presisi (precision), dan recall. Hasil pengujian menunjukkan bahwa model menghasilkan tingkat akurasi sebesar 71,43%, presisi sebesar 73,76%, dan recall sebesar 74,08%. Hasil tersebut menunjukkan bahwa algoritma Naïve Bayes memiliki kemampuan yang cukup baik dalam mendukung proses pengambilan keputusan terkait pengelolaan persediaan susu formula di sektor ritel.Kata kunci: Naive Bayes; Prediksi Stok; Manajemen Persediaan; Toko Serba Ada; Klasifikasi.
Implementation of MobileNetV2 Transfer Learning for Image-Based Classification of Cocoa Fruit Diseases Yoakhina Nicole Makaruku; Jermias Victor Manuhutu; Jenifer Gabriela Neyte
Progresif: Jurnal Ilmiah Komputer Vol 22, No 2 (2026): April
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/progresif.v22i2.3662

Abstract

Cocoa is one of the high-value agricultural commodities in Indonesia; however, its productivity continues to decline due to the increasing prevalence of plant diseases, particularly Black Pod Disease and attacks by Helopeltis spp. Traditional disease detection, which is generally performed manually by farmers, is often inefficient and prone to errors, thereby highlighting the need for an intelligent and technology-assisted early diagnosis system. This study aims to develop a disease classification model for cocoa plants using a Convolutional Neural Network (CNN) based on the MobileNet-V2 architecture, which is recognized for its computational efficiency and strong performance in image analysis. The dataset consisted of 300 images divided into three categories: healthy cocoa pods, Black Pod Disease, and damage caused by Helopeltis. Following the Pareto principle, 80% of the data were allocated for training and 20% for testing. The model was trained for 10 epochs with a batch size of 32 and was supported by data augmentation to improve data variability. Experimental results demonstrated a significant improvement in performance, with the highest validation accuracy of 93.75% achieved at the seventh epoch. The confusion matrix further confirmed that the model classified each category with a high level of precision. These findings indicate that MobileNet-V2 is an effective approach for automatic cocoa disease detection and has strong potential to assist farmers in improving disease management practices in the field.Key words: Convolutional Neural Network; MobileNet-V2; Cocoa Disease Classification; Helopeltis spp.; Deep Learning 
Diagnosa Penyakit Jagung Menggunakan Dempster Shafer Tanaman Pangan Dan Hortikultural Kalimantan Barat Putri Nabila Aprilia; Menur Wahyu Pangestika; Alda Cendekia Siregar
Progresif: Jurnal Ilmiah Komputer Vol 22, No 3 (2026): Juli
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/progresif.v22i3.3833

Abstract

Disease attacks on corn plants can reduce productivity, requiring a system capable of rapid and accurate diagnosis. This study aims to develop a web-based expert system for diagnosing corn plant diseases using the Dempster-Shafer method. The system was built using a knowledge base obtained from experts at the Food Crops and Horticulture Service of West Kalimantan Province. The variables processed included symptom data, disease type, belief value, and control solutions. The diagnosis process was carried out by combining the belief value for each symptom using the Dempster-Shafer combination rule to generate a confidence level for the disease. Testing was conducted by comparing the system's diagnosis results with expert diagnoses on 20 test data sets. The test results showed the system achieved 90% accuracy, indicating that the Dempster-Shafer method is effective in handling symptom uncertainty and is capable of producing accurate corn plant disease diagnoses.Keyword: Corn Plants; Expert System; Dempster Shafer AbstrakSerangan penyakit pada tanaman jagung dapat menurunkan produktivitas sehingga diperlukan sistem yang mampu membantu diagnosis secara cepat dan akurat. Penelitian ini bertujuan mengembangkan sistem pakar berbasis web untuk mendiagnosis penyakit tanaman jagung menggunakan metode Dempster-Shafer. Sistem dibangun menggunakan basis pengetahuan yang diperoleh dari pakar di Dinas Tanaman Pangan dan Hortikultura Provinsi Kalimantan Barat. Variabel yang diproses meliputi data gejala, jenis penyakit, nilai belief, dan solusi pengendalian. Proses diagnosis dilakukan dengan menggabungkan nilai belief setiap gejala menggunakan aturan kombinasi Dempster-Shafer untuk menghasilkan tingkat keyakinan terhadap penyakit. Pengujian dilakukan dengan membandingkan hasil diagnosis sistem terhadap diagnosis pakar pada 20 data uji. Hasil pengujian menunjukkan sistem memperoleh akurasi sebesar 90%, sehingga metode Dempster-Shafer efektif dalam menangani ketidakpastian gejala dan mampu menghasilkan diagnosis penyakit tanaman jagung secara akurat.Kata kunci: Desmpster Shafer; Diagnosis; Penyakit Jagung; Sistem Pakar
Perancangan Sistem Informasi Administrasi Kasir (Point of Sale) Berbasis Web pada Kedai Selera Rasa Ila Nabilla; Anton Zulkarnain Sianipar; Ifan Junaedi
Progresif: Jurnal Ilmiah Komputer Vol 22, No 3 (2026): Juli
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/progresif.v22i3.3791

Abstract

Selera Rasa Restaurant still relied on manual transaction recording and stock management, resulting in data errors and delays in report preparation. This study aimed to design and develop a web-based cashier administration information system using the Laravel framework to automate sales transactions, stock management, and report generation. The system was developed using the Waterfall method. Data were collected through observation, interviews, and literature review. The system was implemented using Laravel, PHP, MySQL, and Bootstrap. System testing was conducted using the Black Box Testing method on all main features. The testing results showed that all system features functioned according to the functional requirements and produced valid results. Based on the testing results, the system was considered suitable for supporting sales transactions, stock management, and automated report generation.Keywords: Information System; Point of Sale; Laravel; Black Box Testing; Cashier AbstrakKedai Selera Rasa masih mengandalkan pencatatan transaksi dan pengelolaan stok secara manual sehingga sering terjadi kesalahan data dan keterlambatan penyusunan laporan. Penelitian ini bertujuan merancang dan membangun sistem informasi administrasi kasir berbasis web menggunakan framework Laravel untuk mengotomatisasi proses transaksi penjualan, pengelolaan stok, dan pembuatan laporan. Metode pengembangan sistem yang digunakan adalah Waterfall. Data penelitian dikumpulkan melalui observasi, wawancara, dan studi pustaka. Sistem dibangun menggunakan Laravel, PHP, MySQL, dan Bootstrap. Pengujian sistem dilakukan menggunakan metode Black Box Testing pada seluruh fitur utama. Hasil pengujian menunjukkan bahwa seluruh fitur sistem berfungsi sesuai dengan kebutuhan fungsional dan memperoleh hasil yang valid. Berdasarkan hasil pengujian tersebut, sistem dinyatakan layak digunakan untuk mendukung proses transaksi penjualan, pengelolaan stok, dan penyusunan laporan secara otomatis.Kata kunci: Sistem Informasi; Point of Sale; Laravel; Black Box Testing; Kasir
Analisis dan Visualisasi Tingkat Kelulusan Mahasiswa Menggunakan Algoritma K-Means Clustering dan Silhoutte Score Berdasarkan Asal Daerah Jermias Victor Manuhutu; Yoakhina Nicole Makaruku; Daniel Halauwet
Progresif: Jurnal Ilmiah Komputer Vol 22, No 2 (2026): April
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/progresif.v22i2.3663

Abstract

Academic achievement of graduates serves as a key indicator of higher education quality and graduates’ readiness to enter the workforce. This study examines the Christian Religious Education Study Program at the State Christian Institute of Ambon (IAKN Ambon) during the 2010–2020 period, focusing on the influence of students’ district of origin on graduation rates and Grade Point Average (GPA). Employing a descriptive quantitative approach based on secondary data, the analysis was conducted using descriptive statistics and the K-Means clustering method, with the optimal number of clusters evaluated through the silhouette score. The findings reveal a concentration of students from several major districts, including Central Maluku, Ambon City, and West Seram Regency. Four performance clusters were identified: high-performing, moderate-performing, challenging, and low-performing/no graduates. An Average Silhouette Score of 0.77 indicates good cluster quality and strong separation among groups. The study highlights disparities in academic achievement based on students’ geographical origins, suggesting the need for more targeted interventions and stronger collaboration with students’ home districts to improve academic success and graduation outcomes.Keywords: Academic Performance; District of Origin; Graduation Rate; K-Means Clustering; Silhouette ScoreAbstrakPrestasi akademik lulusan menjadi indikator utama mutu pendidikan tinggi dan kesiapan lulusan menghadapi dunia kerja. Penelitian ini mengkaji Program Studi Pendidikan Agama Kristen (PAK) Institut Agama Kristen Negeri Ambon periode 2010–2020, dengan fokus pada pengaruh asal kabupaten terhadap tingkat kelulusan dan IPK. Menggunakan pendekatan kuantitatif deskriptif berbasis data sekunder, analisis dilakukan melalui statistik deskriptif dan metode                K-Means clustering, dengan evaluasi jumlah klaster menggunakan silhouette score. Hasil menunjukkan konsentrasi mahasiswa pada beberapa kabupaten utama seperti Maluku Tengah, Kota Ambon, dan Seram Bagian Barat. Ditemukan empat klaster kinerja, yaitu tinggi, moderat, menantang, dan rendah/tanpa lulusan. Nilai Average Silhouette Score sebesar 0,77 menunjukkan kualitas klaster yang baik. Studi ini mengungkap adanya perbedaan capaian akademik berdasarkan asal daerah, sehingga diperlukan intervensi yang lebih terarah serta kerja sama dengan daerah asal mahasiswa untuk meningkatkan keberhasilan studi.Kata kunci: Asal Kabupaten; Clustering: K-Means; Silhoutte Score; Tingkat Kelulusan
Sistem Survei Kepuasan Masyarakat Terhadap Pelayanan Disdukcapil Banjarbaru Dengan K-Means Dan Text Mining Fadilah Fadilah; Ahmad Naufal; Bahar Bahar; Muhammad Arsyad
Progresif: Jurnal Ilmiah Komputer Vol. 22 No. 3 (2026): Juli
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/progresif.v22i3.3523

Abstract

This study develops a public satisfaction survey system for the services of the Banjarbaru City Population and Civil Registration Office (Disdukcapil), which integrates online and offline data collection. The problem encountered is the lack of utilization of collected criticism and suggestions. The purpose of this study is to identify segments of public satisfaction and explore patterns of complaints or appreciation through the application of the K-Means Clustering and Text Mining methods. The methods used in this study are K-Means Clustering to group satisfaction levels and Text Mining to analyze respondents' comments. The results show that the grouping and analysis of public comments provide deeper insights for data-driven public service improvements. This system is expected to assist in the evaluation and continuous improvement of service quality. Keywords: Survey; Population and Civil Registration Office; Banjarbaru; K-Means Clustering; Text Mining Abstrak Penelitian ini mengembangkan sistem survei kepuasan masyarakat terhadap pelayanan Dinas Kependudukan dan Pencatatan Sipil (Disdukcapil) Kota Banjarbaru, yang mengintegrasikan pengumpulan data online dan offline. Masalah yang dihadapi adalah kurangnya pemanfaatan data kritik dan saran yang dikumpulkan. Tujuan penelitian ini adalah untuk mengidentifikasi segmen kepuasan masyarakat dan menggali pola keluhan atau apresiasi melalui penerapan metode K-Means Clustering dan Text Mining. Metode yang digunakan dalam penelitian ini adalah K-Means Clustering untuk mengelompokkan tingkat kepuasan dan Text Mining untuk menganalisis komentar responden. Hasil penelitian menunjukkan bahwa pengelompokan dan analisis komentar masyarakat memberikan wawasan yang lebih dalam untuk perbaikan layanan publik yang berbasis data. Sistem ini diharapkan dapat membantu evaluasi dan peningkatan kualitas pelayanan secara berkelanjutan.
MODEL APLIKASI PEMBELAJARAN HURUF HIJAIYAH DI TAMAN PENDIDIKAN AL-QUR'AN HALABY BANJARBARU Siti Dzakia Salsabila; Khairullah Khairullah; Wahyudi Ariannor; Muhammad Arsyad
Progresif: Jurnal Ilmiah Komputer Vol. 22 No. 3 (2026): Juli
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/progresif.v22i3.3648

Abstract

Learning hijaiyah letters at Taman Pendidikan Al-Qur’an Halaby Banjarbaru previously faced challenges such as low student interest and an unconducive classroom environment due to conventional teaching methods. This study aimed to design and develop a web-based hijaiyah learning application model that enhances interactivity while supporting learning management. The method used was Research and Development with a Waterfall system development model consisting of requirements analysis, design, implementation, and testing stages. The developed application included learning materials, interactive quizzes, teacher and student data management, and learning outcome recording features. The testing results showed that all system functions operated properly, and the application improved learning outcomes for most students. The novelty of this study lies in the integration of interactive learning media and learning management systems within a single platform. Keywords: Hijaiyah Learning; Web Application; Learning Media; Quran Education   Abstrak Pembelajaran huruf hijaiyah di Taman Pendidikan Al-Qur’an Halaby Banjarbaru sebelumnya menghadapi kendala berupa rendahnya minat belajar dan kurang kondusifnya suasana kelas akibat metode pembelajaran yang masih konvensional. Penelitian ini bertujuan untuk merancang dan membangun model aplikasi pembelajaran huruf hijaiyah berbasis web yang mampu meningkatkan interaktivitas serta mendukung pengelolaan pembelajaran. Metode yang digunakan adalah Research and Development dengan model pengembangan sistem Waterfall yang meliputi tahapan analisis kebutuhan, perancangan, implementasi, dan pengujian. Aplikasi yang dikembangkan dilengkapi dengan fitur materi pembelajaran, kuis interaktif, pengelolaan data guru dan peserta didik, serta pencatatan hasil belajar. Hasil pengujian menunjukkan bahwa seluruh fitur sistem berfungsi dengan baik dan penerapan aplikasi memberikan peningkatan hasil belajar pada sebagian besar peserta didik. Kebaruan penelitian ini terletak pada integrasi antara media pembelajaran interaktif dan sistem pengelolaan pembelajaran dalam satu platform.
Klasifikasi Named Entity Recognition (NER) Pada Cerita Pendek Bahasa Indonesia Menggunakan Model Indobert Nazifa Samsurizal; Hendri Ahmadian; Nurrizqa Nurrizqa
Progresif: Jurnal Ilmiah Komputer Vol. 22 No. 3 (2026): Juli
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/progresif.v22i3.3731

Abstract

Entity recognition in Indonesian short stories requires a model capable of understanding narrative language context effectively. This study aimed to implement the IndoBERT model for the Named Entity Recognition (NER) task to identify Person, Location, and Organization entities. The dataset was obtained from the Majalah Bobo e-book and organized using the BIO (Begin, Inside, Outside) format. The research included pre-processing, tokenization, label encoding, token alignment, and fine-tuning using the AutoModelForTokenClassification model. Evaluation was conducted using precision, recall, and F1-score metrics. The results showed that IndoBERT achieved F1-scores of 0.95 for Person, 0.81 for Organization, and 0.75 for Location, with a weighted average F1-score of 0.90. These results indicated that IndoBERT performed entity recognition effectively on Indonesian short stories. Keywords: Named Entity Recognition; IndoBERT; Indonesian short stories; Natural Language Processing; Transformer.    Abstrak Pengenalan entitas pada cerita pendek bahasa Indonesia memerlukan model yang mampu memahami konteks bahasa naratif secara efektif. Penelitian ini bertujuan menerapkan model IndoBERT pada tugas Named Entity Recognition (NER) untuk mengenali entitas Person, Location, dan Organization. Dataset diperoleh dari e-book Majalah Bobo dan disusun menggunakan format BIO (Begin, Inside, Outside). Penelitian dilakukan melalui tahapan pre-processing, tokenisasi, label encoding, token alignment, dan fine-tuning menggunakan model AutoModelForTokenClassification. Evaluasi dilakukan menggunakan metrik precision, recall, dan F1-score. Hasil pengujian menunjukkan bahwa model IndoBERT memperoleh nilai F1-score sebesar 0.95 pada label Person, 0.81 pada Organization, dan 0.75 pada Location, dengan weighted average F1-score sebesar 0.90. Hasil tersebut menunjukkan bahwa IndoBERT mampu melakukan pengenalan entitas dengan baik pada cerita pendek bahasa Indonesia. Kata kunci: Named Entity Recognition; IndoBERT; cerita pendek bahasa Indonesia; Natural Language Processing; Transformer.
Imputasi Curah Hujan ERA5 Menggunakan Random Forest dan XGBoost di Maluku Utara Balya Badar Syah; Brina Miftahurrohmah
Progresif: Jurnal Ilmiah Komputer Vol. 22 No. 3 (2026): Juli
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/progresif.v22i3.3734

Abstract

Climate change increases hydrometeorological disaster risks in North Maluku, yet missing BMKG rainfall data and ERA5's spatial bias hinder precise risk analysis. This study applied point-based Statistical Downscaling using Random Forest (RF) and Extreme Gradient Boosting (XGBoost), with ERA5 precipitation as the single predictor, to impute missing rainfall at four stations. XGBoost achieved lower MSE and RMSE at three stations (Sultan Babullah: MSE 1017.29, RMSE 31.89, MAE 12.31, R² 0.0351; Gamar Malamo: MSE 513.96, RMSE 22.67, MAE 9.91, R² -0.0141; Oesman Sadik: MSE 163.59, RMSE 12.79, MAE 6.27, R² 0.0369), with NRMSE-based accuracy of 83.89 – 87.56%, though RF retained slightly lower MAE. RF was selected at Emalamo (MSE 163.75, RMSE 12.79, MAE 5.62, R² -0.0517, accuracy 85.78%). The models imputed 298 missing days, though the univariate predictor underestimated rainfall above 40 mm. The resulting continuous dataset offers a scientific basis for regional disaster mitigation planning. Key Word: ERA5; Imputation; Statistical Downscaling; Random Forest; XGBoost   Abstrak Perubahan iklim meningkatkan risiko bencana hidrometeorologi di Maluku Utara, sementara kekosongan data curah hujan BMKG dan bias spasial ERA5 menghambat analisis risiko presisi. Penelitian ini menerapkan Statistical Downscaling berbasis titik menggunakan Random Forest (RF) dan Extreme Gradient Boosting (XGBoost), dengan presipitasi ERA5 sebagai prediktor tunggal, untuk mengimputasi kekosongan data curah hujan di empat stasiun. XGBoost menghasilkan MSE dan RMSE lebih rendah di tiga stasiun (Sultan Babullah: MSE 1017,29, RMSE 31,89, MAE 12,31, R² 0,0351; Gamar Malamo: MSE 513,96, RMSE 22,67, MAE 9,91, R² -0,0141; Oesman Sadik: MSE 163,59, RMSE 12,79, MAE 6,27, R² 0,0369), dengan akurasi berbasis NRMSE 83,89 – 87,56%, meski RF tetap mencatat MAE sedikit lebih rendah. RF terpilih di Emalamo (MSE 163,75, RMSE 12,79, MAE 5,62, R² -0,0517, akurasi 85,78%). Model berhasil mengimputasi 298 hari data kosong, meski prediktor univariat underestimate curah hujan di atas 40 mm. Basis data historis kontinu menjadi landasan saintifik bagi mitigasi bencana daerah.  

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