Article Per Year (5 Year)
p-Index From 2021 - 2026
10.34
P-Index
This Author published in this journals
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 J-SAKTI (Jurnal Sains Komputer dan Informatika) 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
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

Published : 138 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 138 Documents
Search

 10   11   12   13   14 
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
Perancangan User Experience Aplikasi Pendaftaran Kunjungan Puskesmas Menggunakan Metode User Experience Lifecycle Sita Widad Muqsithoh; Septi Andryana; Ira Diana Sholihati
SMATIKA JURNAL : STIKI Informatika Jurnal Vol 12 No 01 (2022): SMATIKA Jurnal : STIKI Informatika Jurnal
Publisher : LPPM UBHINUS MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/smatika.v12i01.583

Abstract

The health sector is one of the important sectors of the government which has the potential to be improved by the presence of information technology. The public health center of East Ciputat District, which is one of the health service centers in the South Tangerang City, always improves the technology development. The large number of patient visits it causes problems with queuing visits which take a relatively long time when registering at the service counter. The advantage of the User Experience Lifecycle method that is applied to the development process of the mobile public health center visit online registration application starts from the analysis stage that understands user needs through the data collection method. The design stage involves making Wireframe. The design results are implemented in the form of a mobile based High-Fidelity Prototype and the results are tested using the System Usability Scale (SUS). The prototype evaluation can show the overall results of the application successfully helping users in the efficiency of online registration visitors.
Prediksi Pengadaan Stok Produk Menggunakan Algoritma Single Moving Average Dan Single Exponential Smoothing Septi Andryana; Muhammad Iqbal Nasution; Albaar Rubhasy
SMATIKA JURNAL : STIKI Informatika Jurnal Vol 13 No 02 (2023): SMATIKA Jurnal : STIKI Informatika Jurnal
Publisher : LPPM UBHINUS MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/smatika.v13i02.918

Abstract

Information systems and technology have a crucial role in people's lives because they help make effective decisions. Reliability in predicting Vape sales of liquid products is a priority to improve Vape shop services and profits. However, Vape Industrial does not yet have a method for forecasting the future sales of liquid goods, causing some liquids to experience an excess or shortage. In this study, an application was developed to predict the amount of stock needed in the next period. Consumer transaction data in the previous period were analyzed using the Single Moving Average and Single Exponential Smoothing algorithms. The two algorithms are compared based on the results of error calculations in choosing the right algorithm for product stock prediction. The test results show that this application is successful in predicting Besti Matcha's liquid stock well. For the daily period, the Single Exponential Smoothing algorithm with the alpha 0.2 method gives the right prediction results, with predictions of selling 1 product and an error value of 0%. As for the weekly period, the Single Moving Average algorithm with 5 periods gives predictive results of 5 products sold and an error value of 25%. For the monthly period, Single Exponential Smoothing with an alpha of 0.2 gives prediction results of 21 products sold and an error value of 10.5%. With this application, it is hoped that Vape Industrial can be effective in predicting stock requirements for the next period and avoiding excess product losses.
Improving University Ranking Robustness Using Rank Geometric Weight Integration with CoCoSo Method for Reducing Ordinal Weighting Instability Andryana, Septi; Mantoro, Teddy; Mutiara, Achmad Benny; Ernastuti, Ernastuti; Prihandoko, Prihandoko; Gunaryati, Aris
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 Karim, Muhammad Ibrahim Adham; Andryana, Septi
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.
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
Feature Selection vs Dimensionality Reduction for Steam Game Metadata Classification: An Ensemble Learning Study Handika, Ferdi Setyo; Yulianto, Lili Dwi; Andryana, Septi
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.
Optimizing CNN-LSTM Models for Stock Price Prediction in a Multi-Sector Holding Company Suprapto, Muhamad Fajar; Andryana, Septi
International Journal of Computer Science and Humanitarian AI Vol. 3 No. 1 (2026): IJCSHAI (In Press)
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.
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 Akbar Mulia Ardiansyah Alam Supriyadi Albaar Rubhasy Albaar Rubhasy Aldisa, Rima Tamara Aldjawad, Mohamad 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 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 Finika, Alma Bryan Fitri Freddy Artadima Silaban Ghifari Yusuf Abdillah Gio Fandi H Nainggolan Guntur Wicaksono Gusti Karnawan Handika, Ferdi Setyo Haris Amanullah Ina Agustina Ira Diana Sholihati Ira Diana Sholihati irfan muttaqim Istiarati Ayustina Anjaswari Janubiya, Tasya Khaerani Jhovie Afriyany Karim, Muhammad Ibrahim Adham Koibur, Reni Krisna Ruliyanto 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 Muhamad Afriyan Kahfi 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 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, Ikbal Danu Setiawan, Muhammad Afif Setiyo Budiyanto Sholihati, Ira Diana Shynta Octavia Sita Widad Muqsithoh Suprapto, Muhamad Fajar Supriyadi, Alam Supriyadi, Mohamad Didit Syahbani Farhan Syahputra, Adryan 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 Yulianto, Lili Dwi Yusril Adam Mahendra Yusuf Effendi, Rizal Maulana Zaman, M Alwi Saepul