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All Journal Jurnal Ilmu Komputer dan Sistem Komputer Terapan (JIKSTRA) Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) JURNAL ILMIAH INFOKOM
Bualazatulo Laia
Universitas Potensi Utama
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering

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Kombinasi K-Means dan Fuzzy C-Means untuk Clustering Transaksi PPOB Berdasarkan Validitas Cluster Nanda Setiawan; Heru Fredi; Bualazatulo Laia; Yiska Dayanti Zagoto; Johan; Andreas Jorghy Parapat; Wahyu Saptha Negoro
Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Vol. 4 No. 3 (2026): Februari 2026
Publisher : LKP KARYA PRIMA KURSUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62712/juktisi.v4i3.839

Abstract

Perkembangan layanan Payment Point Online Bank (PPOB) telah mendorong peningkatan signifikan pada volume dan kompleksitas data transaksi digital yang dihasilkan. Algoritma K-Means merupakan salah satu metode clustering yang paling banyak digunakan karena kesederhanaan, efisiensi komputasi, dan kemampuannya dalam menangani data berskala besar. Tujuan penelitian adalah mengelompokkan data transaksi PPOB secara optimal menggunakan kombinasi algoritma K-Means dan Fuzzy C-Means (FCM) serta mengevaluasi kualitas cluster berdasarkan validitas cluster. Data yang digunakan dalam penelitian ini merupakan data transaksi Payment Point Online Bank (PPOB) periode Januari 2024 yang diolah menggunakan Google Colaboratory (Google Colab). Data tersimpan dalam format CSV dan berisi informasi transaksi yang dilakukan oleh berbagai loket PPOB dengan jumlah data: 498.853 data transaksi. Penerapan metode Fuzzy C-Means memberikan hasil yang lebih sesuai karena mampu merepresentasikan derajat keanggotaan ganda pada loket-loket yang berada di zona transisi antar cluster. Keberadaan zona transisi tersebut membuktikan bahwa pendekatan Fuzzy lebih tepat digunakan dalam konteks bisnis PPOB yang dinamis, di mana performa loket dapat berubah seiring waktu dan tidak selalu berada pada kategori yang bersifat mutlak.
PREDIKSI JUMLAH WISATAWAN MANCANEGARA KE INDONESIA MENGGUNAKAN ALGORITMA LINEAR REGRESSION DAN RANDOM FOREST REGRESSION Adil Setiawan; Susiana Khosasih; Marulak Lasron Siahaan; Khoiri Sutan Hasibuan; Bualazatulo Laia; Satriyo Wibowo
INFOKOM (Informatika & Komputer) Vol. 13 No. 1 (2025): JURNAL INFOKOM JUNI 2025
Publisher : POLITEKNIK PIKSI GANESHA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56689/infokom.v13i1.2326

Abstract

Tourism is one of Indonesia’s leading sectors, contributing significantly to the national economy. Forecasting the number of international tourist arrivals is a strategic necessity to support policy planning and the sustainable development of the tourism industry. This study aims to compare the performance of two regression algorithms, Linear Regression and Random Forest Regression, in forecasting international tourist arrivals to Indonesia. The dataset covers the period 2020–2025, obtained from the Central Bureau of Statistics (BPS) with variables that underwent preprocessing such as normalization and handling of missing values. The methodology includes an 80:20 train-test split, tabular regression, and parameter tuning for Random Forest Regression to enhance model performance. Linear Regression was selected as a baseline model, while Random Forest Regression was chosen for its capability to model nonlinear patterns. Model evaluation was conducted using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R² Score. The results show that Linear Regression produced an RMSE of 59,967.668, MAE of 14,837.645, and R² Score of 0.007, indicating low accuracy. In contrast, Random Forest Regression achieved substantially better results with an RMSE of 9,696.530, MAE of 1,193.143, and R² Score of 0.974. These findings confirm that Random Forest Regression provides higher accuracy than Linear Regression, particularly in addressing seasonal patterns and uncertainties caused by global factors. In conclusion, Random Forest Regression can be considered a more reliable forecasting method for predicting international tourist arrivals. The forecasting results can serve as a basis for decision-making in destination capacity planning, foreign exchange revenue estimation, and risk mitigation against global disruptions (pandemics, geopolitical issues, crises), thereby supporting adaptive and sustainable strategies for national tourism development.
Perbandingan Kinerja MetodeTF-IDF dan BM25 pada Pencarian Artikel Berita Bahasa Indonesia Muhammad Al Adib; Yiska Dayanti Zagoto; Bualazatulo Laia; Roslina
Jurnal Ilmu Komputer dan Sistem Komputer Terapan (JIKSTRA) Vol. 8 No. 2 (2026): Edisi Oktober
Publisher : Universitas Harapan Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The growth of online media has increased the number of Indonesian-language news articles and created a need for effective and efficient retrieval systems. This study compares Term Frequency–Inverse Document Frequency (TF-IDF) and Best Matching 25 (BM25) using the Indonesia News Dataset (2025). Of 80,472 articles, 80,463 documents were retained after removing nine articles with empty content. The evaluation used 10 multi-category queries. Ground truth was constructed by pooling the top 20 results from each method, producing 304 query–document pairs manually assessed by one evaluator using relevance labels from 0 to 2. Retrieval effectiveness was measured using Precision@k, Recall@k, Mean Average Precision (MAP), Mean Reciprocal Rank (MRR), and normalized Discounted Cumulative Gain (nDCG). Retrieval time was measured over 30 repetitions after three warm-up runs. At Top-10, BM25 achieved a Precision@10 of 0.79, Recall@10 of 0.351630, MAP@10 of 0.728635, and nDCG@10 of 0.630976, which were higher than the corresponding TF-IDF scores. TF-IDF obtained a slightly higher MRR of 0.90 than BM25 at 0.85. In the evaluated implementation, BM25 required 0.010191 seconds on average, whereas TF-IDF required 0.066014 seconds. Descriptively, BM25 performed better on most Top-10 metrics and retrieval time, but the results depended on query characteristics and experimental configuration.
Co-Authors Andreas Jorghy Parapat Heru Fredi Johan Khoiri Sutan Hasibuan Marulak Lasron Siahaan Muhammad Al Adib Nanda Setiawan Negoro, Wahyu Saptha Roslina, Roslina Satriyo Wibowo Setiawan, Adil Susiana Khosasih Yiska Dayanti Zagoto