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All Journal Techno.Com: Jurnal Teknologi Informasi Jurnal Simetris Jurnal Teknologi dan Manajemen Informatika Jurnal Informatika Jurnal Informatika Jurnal sistem informasi, Teknologi informasi dan komputer Seminar Nasional Informatika (SEMNASIF) SMATIKA KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer JOIV : International Journal on Informatics Visualization SISFOTENIKA Journal of Information Technology and Computer Science (JOINTECS) JURNAL MEDIA INFORMATIKA BUDIDARMA JITK (Jurnal Ilmu Pengetahuan dan Komputer) JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI J I M P - Jurnal Informatika Merdeka Pasuruan INTECOMS: Journal of Information Technology and Computer Science JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) STRING (Satuan Tulisan Riset dan Inovasi Teknologi) Jurnal Mantik Progresif: Jurnal Ilmiah Komputer E-JURNAL JUSITI : Jurnal Sistem Informasi dan Teknologi Informasi Journal of Information Systems and Informatics Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Sistem Komputer & Kecerdasan Buatan Indonesian Journal of Electrical Engineering and Computer Science Jurnal Teknik Informatika C.I.T. Medicom Journal of Intelligent Decision Support System (IDSS) Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Journal of Applied Data Sciences IAIC Transactions on Sustainable Digital Innovation (ITSDI) KLIK: Kajian Ilmiah Informatika dan Komputer J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Informatika Teknologi dan Sains (Jinteks) Eduvest - Journal of Universal Studies Komunita: Jurnal Pengabdian dan Pemberdayaan Masyarakat Jurnal Pengabdian Masyarakat Nasional Jurnal Sistem Informasi dan Teknologi Informasi International Journal of Computer Science and Humanitarian AI Smatika Jurnal : STIKI Informatika Jurnal Jurnal Informatika
Septi Andryana
Universitas Nasional
Aerospace Engineering Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Neuroscience Public Health Social Sciences

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Feature Selection vs Dimensionality Reduction for Steam Game Metadata Classification: An Ensemble Learning Study Ferdi Setyo Handika; Lili Dwi Yulianto; Septi Andryana
Journal of Information System and Informatics Vol 8 No 1 (2026): February
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i1.1456

Abstract

Optimizing noisy Steam game metadata is essential for accurate binary classification. This study compares feature selection (MI) and dimensionality reduction (PCA, LDA) using a dataset of 55,144 Steam reviews and four ensemble algorithms, evaluated through Stratified 5-Fold Cross-Validation. The results show that the 125-feature baseline achieved the highest accuracy of 0.7728 with CatBoost. Feature selection (FS_10) maintained competitive performance with an accuracy of 0.7449, while LDA, after optimization, achieved 0.7281. In contrast, PCA significantly hindered performance (0.6963) due to the inability of linear transformations to preserve the discriminative power of one-hot encoded categorical features, which ensemble models handle better in their original form. These findings highlight the importance of preserving original features, especially in low-to-medium dimensional metadata, where feature selection outperforms dimensionality reduction in maintaining predictive integrity. High accuracy is crucial for developers to track product reception and for platforms to improve recommendation systems that influence user purchasing decisions. The study concludes that for Steam game metadata, strategic feature selection is superior to dimensionality reduction for maintaining classification performance.
ENHANCING COFFEE PRODUCTION FACTOR ASSESSMENT USING LINEAR REGRESSION AND AHP FOR RELIABLE WEIGHT CONSISTENCY Aris Gunaryati; Teddy Mantoro; Septi Andryana; Benrahman; Mohammad Iwan Wahyuddin
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 2 (2025): JITK Issue November 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i2.6788

Abstract

The agricultural sector, particularly coffee production, plays a crucial role in Indonesia’s economy as both a domestic commodity and an export product. However, efforts to optimize coffee production are often constrained by traditional Multi-Criteria Decision-Making (MCDM) methods that rely heavily on subjective judgments, leading to potential inconsistencies—especially in the presence of multicollinearity among variables. This study addresses that challenge by proposing a data-driven and consistent weighting method that integrates Multiple Linear Regression (MLR) with the Analytic Hierarchy Process (AHP). Regression coefficients derived from MLR—based on variables such as the area of immature (-0.2419), mature (0.8357), and damaged (0.5119) coffee plantations—are normalized and incorporated into the AHP pairwise comparison matrix. The resulting Consistency Ratio (CR) values are all below 0.1, indicating high internal consistency and statistical reliability of the derived weights. This integrated approach offers an objective and systematic foundation for MCDM in coffee production analysis, enhances the accuracy of agricultural planning, and supports evidence-based policymaking, while also providing a replicable model for addressing similar challenges in other sectors
Risk Analysis and Mitigation Strategies in Overcoming Cyber Attacks in Information Technology Companies Using the OCTAVE Framework Ikbal Danu Setiawan; Septi Andryana
Eduvest - Journal of Universal Studies Vol. 5 No. 5 (2025): Eduvest - Journal of Universal Studies
Publisher : Green Publisher Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59188/eduvest.v5i5.49957

Abstract

In the increasingly advanced digital era, information technology companies are the main target of increasingly complex and intense cyberattacks. To address these threats, risk analysis is a crucial step to identify, evaluate, and mitigate risks associated with cyberattacks. This study aims to analyze the risk of cyber attacks on information technology companies using the Octave framework. The Octave framework provides systematic and structured risk management guidelines that can be applied to different types of risks, including cyber risks. Based on the results of the risk analysis, priority is given to risks with high severity to obtain appropriate mitigation measures. The proposed risk mitigation strategy includes the implementation of security technologies, the development of security policies and procedures, and the increase in employee awareness and training on cybersecurity practices. In addition, this study emphasizes the importance of continuous monitoring and periodic evaluation of the effectiveness of the mitigation strategies implemented. In an effort to combat cybercrime and protect digital security, this writing uses the Octave framework method. The results of this study show that by using the Octave framework, companies can effectively identify and manage the risk of cyberattacks. The implementation of this framework not only improves information security, but also provides a systematic and structured approach to cyber threats, thereby strengthening the company's resilience to future attacks.
Optimizing CNN-LSTM Models for Stock Price Prediction in a Multi-Sector Holding Company Muhamad Fajar Suprapto; Septi Andryana
International Journal of Computer Science and Humanitarian AI Vol. 3 No. 1 (2026): IJCSHAI
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/ijcshai.v3i1.15060

Abstract

Accurate stock price forecasting is difficult because financial time-series data usually demonstrate nonlinear relationships, irregular fluctuations, and interdependent temporal patterns. This research investigates the predictive performance of three neural network models based on deep learning: Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and a hybrid CNN-LSTM architecture for forecasting stock prices of a multi-sector holding company. The dataset used in this study contains daily historical price observations collected from 2015 to 2025, where sequential samples are generated using a sliding window approach. To obtain appropriate model settings, hyperparameter optimization is carried out using a grid search procedure. Model performance is assessed using Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). Experiments are first performed using an 80:20 training-testing split and followed by a robustness evaluation using a 70:30 data split. Under the primary evaluation scheme, the experimental results indicate that the LSTM model yields the lowest prediction error, reflected by an RMSE value of 77.86, MAE of 58.23, and MAPE of 1.28%. Meanwhile, the hybrid CNN-LSTM model demonstrates more stable performance across different data proportions, achieving an RMSE of 75.71 and MAPE of 1.23% during the robustness test. The results indicate that LSTM is effective in capturing sequential dependencies inherent in financial time-series data, integrating convolutional feature extraction with sequential learning can improve prediction stability under varying training conditions. The results provide empirical insights into the selection of deep learning architectures for stock price prediction in the context of multi-sector holding companies.
Improving University Ranking Robustness Using Rank Geometric Weight Integration with CoCoSo Method for Reducing Ordinal Weighting Instability Septi Andryana; Teddy Mantoro; Achmad Benny Mutiara; Ernastuti Ernastuti; Prihandoko Prihandoko; Aris Gunaryati
Journal of Applied Data Sciences Vol 7, No 1: January 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i1.1024

Abstract

This study lies in the field of decision support systems, focusing on the application of Multi-Criteria Decision Making (MCDM) for ranking alternatives based on predefined organizational criteria. A persistent challenge in this domain is the instability and subjectivity of ordinal weighting methods - such as Rank Order Centroid (ROC), Rank Sum (RS), Rank Reciprocal (RR), and Rank Order Distribution (ROD), which derive weights solely from rank positions, often leading to inconsistent and unreliable outcomes. To address this, this study introduces Rank Geometric (RG) weights, a geometric mean aggregation of ROC, RS, RR, and ROD designed to reduce subjectivity, stabilize weight distribution, and enhance robustness. By using the Combined Compromise Solution (CoCoSo) method, the RG against Times Higher Education’s (THE) official weights were evaluated, and the four individual ordinal methods, applied to the top 10 Indonesian universities across five THE 2025 ranking criteria. Empirical results show that RG-CoCoSo produces stronger and more consistent correlations with THE’s rankings than THE-CoCoSo, as validated by Spearman and Pearson correlation tests, with a p-value of 0.0251. This study contributes a practical, data-driven weighting framework that strengthens the reliability of MCDM-based institutional performance evaluation and can be generalized to other ranking contexts.
Design of Integrated Information System Architecture for BUMDES Management Based on TOGAF ADM Muhammad Ibrahim Adham Karim; Septi Andryana
Simetris: Jurnal Teknik Mesin, Elektro dan Ilmu Komputer Vol. 16 No. 2 (2025): JURNAL SIMETRIS VOLUME 16 NO 2 TAHUN 2025
Publisher : Fakultas Teknik Universitas Muria Kudus

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24176/simet.v16i2.15922

Abstract

Enhancing the accountability and effectiveness of Village-Owned Enterprises (BUMDes) requires an information-system architecture that aligns business processes with public reporting needs. Prior public-sector studies often remain conceptual and rarely demonstrate an end-to-end path from stakeholder requirements to user-validated architectural design. This study proposes a strategy grounded in Enterprise Architecture using the TOGAF-ADM approach. A qualitative, design-oriented method was employed through policy document analysis, stakeholder interviews/observations, and a SWOT assessment feeding the Architecture Vision. The outcome comprises core artifacts across the Business-Data-Application-Technology layers that specify service objectives, information needs, and application support for BUMDes key processes. User acceptance testing indicates a 97% acceptance rate, evidencing strong alignment between the design and operational/reporting requirements. The study contributes a replicable architectural strategy as a foundation for developing integrated information systems in BUMDes settings. Limitations include a single-case context; future work should test generalizability across varying organizational scales and institutional conditions.
Improving University Ranking Robustness Using Rank Geometric Weight Integration with CoCoSo Method for Reducing Ordinal Weighting Instability Septi Andryana; Teddy Mantoro; Achmad Benny Mutiara; Ernastuti Ernastuti; Prihandoko Prihandoko; Aris Gunaryati
Journal of Applied Data Sciences Vol 7, No 1: January 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i1.1024

Abstract

This study lies in the field of decision support systems, focusing on the application of Multi-Criteria Decision Making (MCDM) for ranking alternatives based on predefined organizational criteria. A persistent challenge in this domain is the instability and subjectivity of ordinal weighting methods - such as Rank Order Centroid (ROC), Rank Sum (RS), Rank Reciprocal (RR), and Rank Order Distribution (ROD), which derive weights solely from rank positions, often leading to inconsistent and unreliable outcomes. To address this, this study introduces Rank Geometric (RG) weights, a geometric mean aggregation of ROC, RS, RR, and ROD designed to reduce subjectivity, stabilize weight distribution, and enhance robustness. By using the Combined Compromise Solution (CoCoSo) method, the RG against Times Higher Education’s (THE) official weights were evaluated, and the four individual ordinal methods, applied to the top 10 Indonesian universities across five THE 2025 ranking criteria. Empirical results show that RG-CoCoSo produces stronger and more consistent correlations with THE’s rankings than THE-CoCoSo, as validated by Spearman and Pearson correlation tests, with a p-value of 0.0251. This study contributes a practical, data-driven weighting framework that strengthens the reliability of MCDM-based institutional performance evaluation and can be generalized to other ranking contexts.
Improving MCDM University Rankings through Statistical Validation Using Spearman’s Correlation and THE Benchmark Septi Andryana; Teddy Mantoro; Aris Gunaryati; Alfarizky Esah Raffliansyah
Journal of Applied Data Sciences Vol 6, No 3: September 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i3.796

Abstract

The evaluation of higher education institutions is a critical field for informing data-driven policy and institutional benchmarking. A key problem in this area is the lack of transparency and consistency in university rankings, particularly when using Multi-Criteria Decision-Making (MCDM) methods such as MABAC and MAIRCA, with limited research on how weighting techniques affect the reliability and alignment of these rankings with international standards like the Times Higher Education (THE) Rankings. This study proposes the use of MABAC and MAIRCA methods combined with two weighting techniques—Rank Order Centroid (ROC) and Rank Sum (RS)—to assess 20 top Indonesian universities based on five performance indicators: research quality, research environment, teaching, industry, and international outlook. Spearman’s rank correlation is used to compare the MCDM-generated rankings with THE Rankings 2025. The study contributes empirical evidence on the impact of weighting schemes on the consistency and reliability of university rankings and demonstrates that the MAIRCA-ROC method achieves the highest agreement with THE Rankings, with a correlation coefficient of 0.8135 and a p-value of 0.00001. These results validate the use of MCDM methods in higher education evaluation and emphasize the importance of selecting appropriate weighting techniques to develop transparent and robust ranking frameworks that support evidence-based policy decisions.
Sentiment Analysis of Indonesian Mobile Banking Reviews on Google Play Store in 2025: A Comparison of Machine Learning Algorithms Aji Putra Wicaksana; Andryana, Septi
Jurnal Informatika Vol. 13 No. 2 (2026): October
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/informatika.v13i2.13512

Abstract

Mobile banking applications generate large volumes of user reviews that can be used to evaluate digital banking service quality. This study analyzes sentiment in Indonesian mobile banking reviews on the Google Play Store in 2025 and compares the performance of Naive Bayes (NB), Support Vector Machine (SVM), and Random Forest (RF). A total of 270,977 reviews were collected from five applications, namely BRImo, Livin’ by Mandiri, myBCA, BYOND by BSI, and wondr by BNI. The data were processed through text cleaning, case folding, tokenization, word normalization, stopword removal, and negation retention. Sentiment labels were generated automatically using the InSet Lexicon and validated on 450 stratified review samples. The validation results showed moderate agreement between automatic and manual labels, with an accuracy of 0.6111, macro-F1 score of 0.5384, and Cohen’s Kappa of 0.4167. Review texts were represented using TF-IDF with unigram and bigram features, and model performance was evaluated across six stratified data-splitting scenarios using accuracy, macro precision, macro recall, macro-F1, confusion matrix, and macro ROC-AUC. The results show that RF in Scenario 6 achieved the best performance, with an accuracy of 0.9524, macro-F1 score of 0.8498, and macro ROC-AUC of 0.9798. Dominant-term analysis indicates recurring complaints related to login failures, transaction errors, system disruptions, and application updates. This study contributes a cross-application comparison of machine learning algorithms for Indonesian mobile banking sentiment analysis and provides practical insights for improving service reliability. However, the findings should be interpreted within the limitation of automatic lexicon-based labeling, particularly for neutral and context-dependent reviews. 
Co-Authors A. Yusuf, Ridwan Abdul Rahman Wijaya Putra Achmad Benny Mutiara Achmad Riyadi Aditya Pratama Aditya Pratama Adryan Syahputra Agung Sembiring Agus Iskandar Ahmad Fahreza Aji Afani Maldini, Muhamad Aji Putra Wicaksana Akbar Mulia Ardiansyah Alam Supriyadi Albaar Rubhasy Albaar Rubhasy Aldisa, Rima Tamara Aldjawad, Mohamad Alfarizky Esah Raffliansyah Alfi Nur Rahmawan Ali, Muhammad Rifqi Alma Bryan Fitri Finika Andani, Angela Nerci Andrianingsih Andrianingsih Angga Pradihta Anugrah Putra, Mandala Aprilia Tika Rahayu Apriliani Wulandari Arif Setyo Pambudi Aris Gunaryat Aris Gunaryati Aris Gunaryati Aris Gunaryati Aris Gunaryati Aris Gunaryati Aris Gunaryati, Aris Avicienna Adhi Narendro Purnomo Azharandi, Nadhif badriawan sukma Bagus Tegar Dwi Irianto Bagus Widiyanto Bayu Setyawan Ben Rahman Ben Rahman Benrahman Benrahman Bimo Anggoro Seno Bimo Salasa Putra Budi Dwi Kurniawan Caniago, Diana Deny Hidayatullah Deny Hidayatullah Dewantoro, Danang Dhani Farhansyah Diana Caniago Dicky Pradana Abdulrahman Julianto Dwi Yulian Rizki Saputra Era, Defrimont Eri Mardiani Ernastuti Ernastuti Esah Raffliansyah, Alfarizky Farhany, Nadia Muis Fauziah Fauziah Febfiyana Febfiyana Fendy Sulistyo Ferdi Setyo Handika Finika, Alma Bryan Fitri Freddy Artadima Silaban Ghifari Yusuf Abdillah Gio Fandi H Nainggolan Guntur Wicaksono Gusti Karnawan Haris Amanullah Ikbal Danu Setiawan Ina Agustina Ira Diana Sholihati Ira Diana Sholihati irfan muttaqim Istiarati Ayustina Anjaswari Janubiya, Tasya Khaerani Jhovie Afriyany Koibur, Reni Krisna Ruliyanto Lili Dwi Yulianto Lukman Medriavin Silalahi M Alwi Saepul Zaman Mahendra, Wahyu Eka Mamisala, Dion R A Maurizka Zahra Devita Mikhael Kristian Misria Attanggo Mochamad Naufal Shofy Mohamad Aldjawad Mohamad Didit Supriyadi Mohammad Iwan Wahyuddin Mohammad Iwan Wahyuddin Muhamad Afriyan Kahfi Muhamad Fajar Suprapto Muhammad Ibrahim Adham Karim Muhammad Ikbal Muhammad Ikbal Muhammad Iqbal Nasution Muhammad Reza Akbar Muhammad Rifqi Ali Muhammad Taufiq Ismail Mursyid Salim Nadhif Azharandi Nadia Aulia Nadila, Tika Aenun Naif Farhan Naqiyah, Shofy Navita Putri Purwanti Nur Hayati Nur Widya Pratiwi Nur, Ridwan Pramesti Sifa Aisya Nuha Pratiwi, Ridha Prihandoko . Puspita, Anna Thasyia Putra, Abdul Rahman Wijaya Putra, Ricky Syah Raffliansyah, Alfarizky Esah Rafli Ramadhan Gunawan RAHAYU, FAJAR Rahman, Ben Rahmat Azyad Samallo Ratih Titi Komala Sari, Ratih Titi Komala Ratih Titi Komalasari Rehan Aria Khalif Resa Wahyu Adi Pujianto Ricky Syah Putra Ridwan Baharudin Ridwan Nur Rifqi Firmansyah Rifqi Zulfikar Rima Tamara Aldisa Rima Tamara Aldisa Rivan Wahyuda Riyadi, Achmad Rizal Maulana Yusuf Effendi Rizki Insani Maulana Ryan maulana Sari Ningsih Selamet Kurniawan Setia Sri Anggraeni Setiawan, Muhammad Afif Setiyo Budiyanto Sholihati, Ira Diana Shynta Octavia Sita Widad Muqsithoh Supriyadi, Alam Supriyadi, Mohamad Didit Syahbani Farhan Syahputra, Adryan Teddy Mantoro Teddy Mantoro Teddy Mantoro Teguh Riyono Adi Tika Aenun Nadila Topan Yulianto Ucuk Darusalam Wafiqah Setyawati Wahyuni Wahyu Eka Mahendra Wahyuddin, Mohammad Iwan Wattimena, Fegie Yoanti Winarsih Winarsih Winarsih Winarsih Winarsih Winarsih Yuda Aji Pramukti Yusril Adam Mahendra Yusuf Effendi, Rizal Maulana Zaman, M Alwi Saepul