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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) BERKALA FISIKA Media Statistika MATEMATIKA JURNAL SISTEM INFORMASI BISNIS Semantik Jurnal Ilmiah Kursor Jurnal Inovasi Teknologi Pendidikan Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Edukasi dan Penelitian Informatika (JEPIN) Register: Jurnal Ilmiah Teknologi Sistem Informasi Proceeding of the Electrical Engineering Computer Science and Informatics AKSIOMA Jurnal Teknologi dan Sistem Komputer Informatika Mulawarman: Jurnal Ilmiah Ilmu Komputer Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Matematika: MANTIK Jurnal Penelitian Pendidikan IPA (JPPIPA) Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control BAREKENG: Jurnal Ilmu Matematika dan Terapan Unisda Journal of Mathematics and Computer Science (UJMC) JTAM (Jurnal Teori dan Aplikasi Matematika) Jurnal Sisfokom (Sistem Informasi dan Komputer) Jurnal Segara Indonesian Journal of Electrical Engineering and Computer Science Journal of Applied Data Sciences Journal of Soft Computing Exploration Interdisciplinary Social Studies Journal of Fundamental Mathematics and Applications (JFMA) Buletin Abdi Masyarakat (BAM) Prosiding Konferensi Nasional Penelitian Matematika dan Pembelajarannya Jurnal Ekonomi, Teknologi dan Bisnis Scientific Journal of Informatics
Bayu Surarso
Department Of Mathematics & Graduate School Of Information System
Agriculture, Biological Sciences & Forestry Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Public Health Social Sciences Transportation Other

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Toward an Adaptive IPO-Based Information Systems Framework for Customer Churn Management Syibli, Mohammad; Gernowo, Rahmat; Surarso, Bayu; Setiawan, Aldi; Setiabudi, Nur Andi
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 15 No. 01 (2026): JANUARY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v15i01.2552

Abstract

The high churn rate in the telecommunications industry remains a persistent challenge affecting customer retention, revenue stability, and long-term competitiveness. Despite extensive research, most customer churn management (CCM) studies in the telecom sector focus narrowly on improving model accuracy, overlooking organizational, strategic, and adaptive dimensions essential for effective management. This paper presents a systematic literature review (SLR) of academic publications from 2020 to 2025, analyzed through the Input–Process–Output (IPO) framework, to synthesize state-of-the-art developments in CCM from an Information Systems perspective. Twenty high-impact studies were coded across industries, emphasizing telecommunications, to examine data inputs, analytical processes, outputs, and feedback or retraining mechanisms. The findings reveal a strong bias toward predictive modelling using ensemble machine learning techniques (e.g., Random Forest, XGBoost, LightGBM) and limited exploration of explainable AI tools (SHAP, LIME), adaptive retraining, and business validation. This imbalance highlights the need for a holistic, adaptive framework integrating analytical intelligence with managerial decision-making. The study contributes by proposing a synthesized reference model and future research agenda for developing adaptive, information-systems-based churn management frameworks in the telecommunications industry.
Hybrid heuristic model and Fuzzy C-Means for stock forecasting using Type 2 Fuzzy Time Series Rineka Brylian Akbar Satriani; Farikhin Farikhin; Bayu Surarso
Interdisciplinary Social Studies Vol. 4 No. 1 (2024): Regular Issue: October-December 2024
Publisher : International Journal Labs

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55324/iss.v4i1.742

Abstract

Forecasting is important in investment because of the inconsistent stock price pattern that requires in-depth analysis. This study proposes using a combination of heuristic and Fuzzy C-Means (FCM) models on Fuzzy Time Series Type 2. This study aims to obtain accurate forecasting results by using more data from the time series. The results show that the proposed model provides accurate forecasting. The FCM model is used to group data into clusters and form intervals. Heuristics also optimizes the performance of Fuzzy Logical Relationships Group (FLRG) by using up and down trends. Type 2 FTS is an extension of  Type 1 that uses union and intersection operators to refine fuzzy relations. The results show that the modification by combining FCM and heuristics in Type 2  FTS for stock forecasting provides excellent results with a MAPE value of 2,87%.
A hybrid divisive K-means framework for big data–driven poverty analysis in Central Java Province Winarno, Bowo; Warsito, Budi; Surarso, Bayu
Indonesian Journal of Electrical Engineering and Computer Science Vol 41, No 1: January 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v41.i1.pp258-269

Abstract

Clustering is essential in big data analytics, especially for partitioning high dimensional socioeconomic datasets to support interpretation and policy decisions. While K-Means is widely used for its simplicity and scalability, its strong sensitivity to initial centroid selection often leads to unstable results and slower convergence. Previous hybrid approaches, such as Agglomerative–K-Means, attempted to address this issue by using hierarchical clustering for centroid initialization; however, these methods rely on bottom-up merging, which can produce suboptimal initial partitions and increase computational overhead for larger datasets. To overcome these limitations, this study proposes a hybrid divisive–K-Means (DHC) model that employs top-down hierarchical splitting to generate more coherent initial centroids before refinement with K-Means. Using a multidimensional poverty dataset from Central Java Province provided by the Indonesian Central Bureau of Statistics (BPS), the performance of DHC was evaluated against standard K-Means and Agglomerative–K-Means. The assessment included execution time, convergence iterations, and cluster validity indices (Silhouette, Davies–Bouldin, and Calinski–Harabasz). Experimental results demonstrate that DHC reduces execution time by up to 97% and requires 40% fewer iterations than standard K-Means, while achieving comparable or improved cluster quality (e.g., CH Index increasing from 14.3 to 15.8). These findings indicate that the DHC model offers a more efficient and stable clustering solution, addressing the shortcomings of previous standard K-Means methods and improving performance for large-scale socioeconomic data analysis.
ANALYSIS OF FACTORS AFFECTING THE SELLING PRICE OF FROZEN BIG EYE TUNA (TUNNUS OBESUS) AT CILACAP FISHING PORT Shalichaty, Shiffa Febyarandika; Saputra, Suradi Wijaya; Wijayanto, Dian; Surarso, Bayu
Jurnal Segara Vol 20, No 2 (2025): December
Publisher : Politeknik Kelautan dan Perikanan Dumai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15578/segara.v20i2.19438

Abstract

Bigeye tuna is one of Indonesia's leading fishery commodities. One of the main locations for the routine landing and handling of this species is the Cilacap Fishing Port. The selling price of bigeye tuna plays a crucial role in the sustainability of fisheries businesses. However, unstable and unpredictable tuna prices make it difficult for some businesses to assess their long-term sustainability. Various factors influence tuna prices, both internal to fishing operations and external economic conditions. This study aims to identify factors that influence the selling price of bigeye tuna at the Cilacap Fishing Port. This study used direct observation, interviews, and a literature review, with data ranging from 2022 to 2024. The analytical approach used was multiple linear regression analysis. The research findings indicate that the selling price of frozen bigeye tuna is influenced by production volume, production value, fishing month, and operational costs. The F-test results indicate that the dollar exchange rate, export volume and value, production volume, production value, fishing month, and operational costs simultaneously influence the selling price of bigeye tuna. These variables influence 80.6% of the selling price of bigeye tuna, while 19.4% is influenced by other factors outside the research.                   
Customer Segmentation Based on Recency, Frequency, Monetary Analysis Using K-Means Algorithms in Apple Ecosystem Edwin Setiawan; Bayu Surarso; Dinar Mutiara Kusumo Nugraheni
Jurnal Penelitian Pendidikan IPA Vol 11 No 2 (2025): February
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v11i2.10011

Abstract

One of the companies in Semarang engaged in gadget sales services has an Apple Ecosystem information system for selling products from an exclusive brand, Apple. Inside there are sales transactions and also service devices iPad, Macbook Air, Macbook Pro, AirPods, Mac, and Apple Accsessories. This research uses purchase transaction data from Apple Ecosystem customers for the period 2023. The use of RFM (Recency, Frequency, Monetary) analysis helps in determining the attributes used for customer segmentation. To determine the optimal number of clusters from the RFM dataset, the Elbow method is applied. The dataset generated from RFM is grouped using the K-Means algorithm, the quality of the algorithm will be compared in cluster formation using the Silhouette Coefficient method. All procedures will be loaded into the Customer Segmentation App (RFM Clustering) web application. Customer segmentation from RFM datasets that have been clustered produces 3 optimal clusters, namely Cluster 2 is High Spenders with 326 customers, Cluster 0 is VIP Customers, Cluster 1 is Frequent Buyers. Cluster validation of k-means using the silhouette coefficient produces a value of 0.3524.
Integration of BERTopic and IndoBERTweet for Aspect-Based Sentiment Analysis (ABSA) on Short Text Data: A Case Study of Responses to Government Policies in 2025 Nabiel Putra Adam; Rahmat Gernowo; Bayu Surarso
Jurnal Ekonomi Teknologi dan Bisnis (JETBIS) Vol. 5 No. 1 (2026): Jurnal Ekonomi, Teknologi dan Bisnis
Publisher : Al-Makki Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57185/k9s34530

Abstract

The implementation of various government policies in 2025 has triggered massive public opinion on social media platform X; however, traditional sentiment analysis often fails to provide details on specific topics, necessitating an Aspect-Based Sentiment Analysis (ABSA) approach. This research integrates the BERTopic model for aspect extraction and IndoBERTweet for sentiment classification to address the challenges associated with the characteristics of short and unstructured text. By preserving the data without a stemming process to maintain semantic context integrity, the BERTopic model demonstrates optimal performance with a Coherence score (C_v) of 0.7539 and a Topic Diversity of 0.9285. The synergy between BERTopic and IndoBERTweet proves effective in generating coherent topic representations and accurate sentiment classification for informal language on social media. Consequently, this integration provides a more profound and superior solution for mapping public responses to the dynamics of government policy.
Analysis of Naïve Bayes and K-Nearest Neighbors Algorithms for Classifying Fishermen Aid Eligibility Muhammad Nasrullah; Bayu Surarso; Oky Dwi Nurhayati
Jurnal Penelitian Pendidikan IPA Vol 10 No 10 (2024): October
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v10i10.8818

Abstract

This article analyzes the use of data mining with Naïve Bayes and K-Nearest Neighbor (KNN) algorithms to build classification models and evaluate their performance in identifying fishermen eligible for aid. The study aims to compare the effectiveness of these algorithms in handling imbalanced datasets using the Synthetic Minority Over-sampling Technique (SMOTE). The research applies SMOTE to improve the balance of the dataset before classification. Without SMOTE, Naïve Bayes achieved an accuracy of 97.01%, precision of 94.16%, recall of 96.67%, and F1-score of 95.39%. KNN, on the other hand, reached an accuracy of 94.04%, precision of 94.53%, recall of 86.00%, and F1-score of 90.06%. After applying SMOTE, both algorithms improved: Naïve Bayes attained an accuracy of 98.33%, precision of 96.86%, recall of 100.00%, and F1-score of 98.49%, while KNN reached an accuracy of 96.90%, precision of 97.72%, recall of 96.19%, and F1-score of 96.94%. The results show that Naïve Bayes, with SMOTE, outperforms KNN in managing data imbalance and accurately classifying eligible fishermen for aid.
Co-Authors A. Nafis Haikal Adi Wibowo Adi Wibowo Agus Subagio Ahmad Abdul Chamid Ahmad Aviv Mahmudi Aldi Setiawan, Aldi Alfajri, Willy Bima Ali Bardadi Anak Agung Gede Sugianthara Antariksa, Muhammad Deagama Surya Arief Hidayat Aris Puji Widodo Aris Sugiharto Aslam Fatkhudin Aulia, Lathifatul Badieah Assegaf Bambang Irawanto Beta Noranita Budi Warsito Budi Warsito Budi Warsito Che Pee, Ahmad Naim Dedy Kurniadi Dian Wijayanto Dinar Mutiara Kusumo Nugraheni Dwi Putri Handayani Dwiyanasari, Desty Edwin Setiawan Eko Adi Sarwoko Eko Sediyono Etna Vianita Fajar Nugraha Fra Siskus Dian Arianto Ghufron Ghufron Harjito - Henny Indriyawati Imam Tahyudin Indah Jumawanti Irfan Santiko I’tishom Al Khoiry Jumawanti, Indah Jumawanti, Indah Juwanda, Farikhin Khoerunnisa, Selvi Fitria Khusnah, Miftakhul Laily Rahmania, Laily Lili Rusdiana, Lili LM Fajar Israwan, LM Fajar Lucia Ratnasari Masruroh, Fitriana Maunah, Uun Migunani Migunani Muhammad Haris Qamaruzzaman Muhammad Nasrullah Muhammad Sam'an Mustafid Mustafid Mustaqim Mustaqim Mustaqim Mustaqim, Mustaqim Nabiel Putra Adam Nugraheni, Dinar Oky Dwi Nurhayati Pukky Tetralian Bantining Ngastiti Putri, Aina Latifa Riyana Putri, Nitami Lestari Putut Sriwasito Rachmat Gernowo Ragil Saputra Ragil Saputra Rahmat Gernowo Rahmawati, Nurhita Ratri Wulandari Rezki Kurniati, Rezki Rineka Brylian Akbar Satriani Robertus Heri Sulistyo Utomo Saputra, Ragil Satriani, Rineka Brylian Akbar Setiabudi, Nur Andi Shiffa Febyarandika Shalichaty Siti Alfiatur Rohmaniah St. Budi Waluya Sulastri Daruni Sulistiyo, Budi Suradi Wijaya Saputra Suryono Suryono Suryono Suryono Suryono, Suryono Susi Hendartie Susilo Hariyanto sutimin sutimin Sutrisno, Sutrisno Sutrisno, Sutrisno Syibli, Mohammad T Indriastuti . Titi Udjiani SRRM Tri Retnaningsih Soeprobowati Uswatun Khasanah Vianita, Etna Wahyul Amien Syafei Wicaksono, Mahad Winarno, Bowo Zainal Arifin Hasibuan