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AUDIT KEAMANAN SISTEM INFORMASI DENGAN COBIT 2019 PADA DOMAIN APO13 DI PT SARASWANTI INDOLAND DEVELOPMENT TBK Bagus Setya; Feby Charlos; Lisdianto Dwi Kesumahadi
Journal of Innovation And Future Technology Vol. 8 No. 1 (2026): Vol 8 No 1 (Februari 2026): Journal of Innovation and Future Technology (IFTECH
Publisher : LPPM Unbaja

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47080/iftech.v8i1.4476

This study aims to evaluate the quality of data and information security at PT Saraswanti Indoland Development Tbk through the development of an Information System (IS) based on the COBIT 2019 framework. Data and information security play a critical role in maintaining business continuity in the current digital era, particularly for companies operating in the property development industry such as PT Saraswanti Indoland Development Tbk. The selection of COBIT 2019 is based on its comprehensive approach to information system security, encompassing areas such as planning and organization, acquisition and implementation, delivery and support, as well as monitoring and evaluation. The objective of this research is to identify weaknesses in existing information system procedures, specifically focusing on data and information security, and to provide recommendations for improvement measures that can be implemented to strengthen security practices. This study employs a qualitative analysis method conducted through in-depth interviews with key stakeholders and the distribution of questionnaires. Based on the findings, recommendations for the design of an information system security framework with more stringent security standards are formulated. By implementing an information system security audit designed in accordance with COBIT 2019, PT Saraswanti Indoland Development Tbk aims to enhance the quality of data and information security, thereby supporting the company’s long-term sustainability and growth.

BUSINESS PROCESS REENGINEERING MODELING FOR ERP SYSTEM IMPLEMENTATION AT YOGYAKARTA SPECIAL PROVINCE HEALTH AGENCY Feby Charlos; Lisdianto Dwi Kesumahadi; Bagus Setya
Journal of Innovation And Future Technology Vol. 8 No. 1 (2026): Vol 8 No 1 (Februari 2026): Journal of Innovation and Future Technology (IFTECH
Publisher : LPPM Unbaja

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47080/iftech.v8i1.4481

The implementation of Enterprise Resource Planning (ERP) systems in public health organizations is crucial for improving operational efficiency, integrating data, and enhancing service transparency. However, the challenges of digital transformation and bureaucratic processes require ongoing improvements through Business Process Reengineering (BPR). This study focuses on modeling the ERP system at the Health Agency of Yogyakarta Special Province, based on data from 2013–2014, and aligns it with Indonesia’s digital transformation initiatives in the health sector for 2025. Using a descriptive-analytic approach, the research employs BPR methodology to identify inefficiencies and propose optimized ERP workflows. The analysis includes process mapping, gap analysis, and functional modeling, ensuring alignment with national e-Government strategies.The findings show that the existing ERP system has effectively integrated data flows across administrative units. However, specific modules, especially in human resources and medical logistics, require reengineering to enhance real-time data exchange and decision-making. The study highlights the importance of combining ERP systems with BPR to continuously improve digital maturity and support data-driven governance in regional health institutions. This research contributes both a conceptual and practical framework for modernizing ERP systems within Indonesia’s public health sector, providing

DESIGN OF AN ERP-BASED INTEGRATED INFORMATIONSYSTEM FOR HEALTH DATA MANAGEMENT ATLUBUKLINGGAU HEALTH AGENCY Yuda Pratama Wibawa; Feby Charlos
Journal of Innovation And Future Technology Vol. 8 No. 1 (2026): Vol 8 No 1 (Februari 2026): Journal of Innovation and Future Technology (IFTECH
Publisher : LPPM Unbaja

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47080/iftech.v8i1.4486

This research focuses on the design and development of an integrated ERP (Enterprise Resource Planning)-based information system for managing health data at the Lubuklinggau City Health Agency. The key issue addressed is the fragmented nature of health data management and the absence of an integrated system within local government agencies. Inadequate integration of health data can lead to inefficiencies, errors, and delayed decision-making, which impacts public health services. This study identifies the core challenges related to the inefficiency and inaccuracy of health data management at the local government level. The research employs a system design methodology combined with a qualitative approach. It begins with an in-depth user needs analysis to understand the specific requirements of the Health Agency. Based on this analysis, an ERP model is developed, focusing on integrating various health data management functions such as patient information, financial records, and health statistics into a unified system. The system is designed to improve data accuracy, streamline operations, and facilitate real-time reporting, contributing to more informed decision-making. The expected outcome is the creation of a functional ERP-based system that enhances the integration and efficiency of health data management at the Lubuklinggau City Health Agency. This research provides practical solutions for overcoming current data management challenges, offering a model that can be replicated by other local health agencies. Furthermore, it introduces an innovative ERP approach to the public health sector, demonstrating its potential to improve both the operational effectiveness and service delivery in local government health departments. This research significantly contributes to advancing health information systems by integrating ERP solutions into public sector health management.

Implementation of K-Means Algorithm in Data Mining for Drug Market Segmentation Joko Prasetiana; Feby Charlos
Journal of Digital Technology and Computer Science Vol. 3 No. 2 (2026): April 2026
Publisher : Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66053/dtcs.v3i2.582

Purpose – This study aims to implement the K-Means clustering algorithm in data mining to segment pharmaceutical products based on stock and sales patterns. The study addresses the need for data-driven product classification to support more effective inventory management and marketing decision-making in pharmaceutical businesses. Methods – This research applied a quantitative data mining approach using secondary sales transaction data from a pharmaceutical distributor covering the period from January 2022 to December 2023. The dataset consisted of 1,248 transaction records, which were aggregated into 12 pharmaceutical products based on stock quantity and sold quantity variables. Data preprocessing included cleaning, transformation, aggregation, and scale checking through Min-Max normalization. The reported K-Means calculation was presented using original-scale stock and sold quantity values for interpretability, while the optimal number of clusters was determined using the Elbow Method and validated with the Silhouette Score. Findings – The Elbow Method indicated that three clusters were optimal, supported by a Silhouette Score of 0.71. The clustering results classified products into high-demand, moderate-demand, and low-demand segments. High-demand products require prioritized stock replenishment and distribution, while low-demand products need tighter inventory control and targeted promotional strategies. Research implications – The findings provide practical insights for improving procurement planning, inventory optimization, and promotional decision-making. However, the analysis is limited to 12 aggregated products and two variables. Originality – This study contributes by applying K-Means clustering specifically to pharmaceutical product-level market segmentation using stock and sales data.

Does Digital Pedagogy Matter for EFL Writing? Cybergogy, Student Engagement, and High School Students’ Writing Skills Suryadi; Feby Charlos
VELES Voices of English Language Education Society Vol 10 No 1 (2026): April 2026
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/veles.v10i1.29841

Writing remains one of the most demanding skills in EFL learning because it requires students to coordinate linguistic accuracy, idea development, organization, and sustained cognitive effort. In digitally mediated learning contexts, effective pedagogy and students’ active engagement may play important roles in supporting writing development. This study examined the correlations among cybergogy, student engagement, and English writing skills among high school students in Serang City, Indonesia. Using a quantitative correlational design, the study included 330 Grade XI students selected from a population of 1,887. Data were collected using a validated 12-item questionnaire with a five-point Likert scale and analyzed in SPSS 26. The analysis included normality testing, Pearson correlation, t-tests, ANOVA, and coefficient of determination. The findings showed that cybergogy was significantly and positively correlated with students’ writing skills (r = 0.564, p < 0.001), whereas student engagement showed a stronger positive correlation (r = 0.688, p < 0.001). Simultaneously, cybergogy and student engagement were significantly associated with writing skills (R = 0.763, R² = 0.582, F = 151.121, p < 0.001), explaining 58.2% of the variance in students’ writing skills. These results indicate that EFL writing development is associated not only with the quality of digitally mediated pedagogy but also with students’ behavioral, emotional, and cognitive engagement. The study implies that English teachers should design digital writing instruction that combines structured online activities, interactive feedback, collaborative practice, and engagement-oriented learning tasks to support students’ writing development.

Decision Support System for Laptop Selection Using the TOPSIS Method on Web-Scraped iPrice Data Feby Charlos; Riska Septiani
Journal of Digital Technology and Computer Science Vol. 3 No. 2 (2026): April 2026
Publisher : Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66053/dtcs.v3i2.666

Purpose – This study develops and revises a web-based decision support system for laptop selection by integrating web-scraped iPrice Indonesia product data, reproducible preprocessing, and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The study responds to the difficulty consumers face when comparing many laptop alternatives with heterogeneous specifications, prices, and budget constraints. Methods – The prototype used a verified static CSV dataset derived from public product listings. Five complete laptop alternatives were evaluated with 12 criteria: brand, screen size, screen resolution, processor, storage type, storage capacity, graphics card, laptop weight, battery durability, new-price distance, preloved-price distance, and RAM. Categorical attributes were transformed into ordinal scores. TOPSIS was implemented in Python and Streamlit. Simple Additive Weighting (SAW), sensitivity analysis, and functional black-box testing were used as comparative and verification procedures. Findings – Under equal criterion weights of 1.5, a new-laptop budget of IDR 7,500,000, and a preloved-laptop budget of IDR 5,000,000, HP Envy x360 13-inch obtained the highest TOPSIS closeness coefficient of 0.746618238. SAW selected the same top alternative, although the complete ranking differed and produced a moderate Spearman correlation of 0.400. Research implications – The results show that transparent criterion transformation, budget-distance modeling, and interface-based preference adjustment can support practical laptop selection. The system does not replace consumer judgment because the ranking depends on the dataset, weights, scoring rules, and budget assumptions. Originality – This study contributes a reproducible DSS prototype that combines scraped price-comparison data, TOPSIS ranking, SAW benchmarking, sensitivity checking, and an Indonesian-language Streamlit interface for practical laptop recommendation.

PERBANDINGAN RANDOM FOREST, NAIVE BAYES, DAN HEURISTIC POUR UNTUK ANALISIS SENTIMEN MBG Ita Hardianti; Feby Charlos; Kanim Kanim
Biner : Jurnal Ilmiah Informatika dan Komputer Vol. 5 No. 2 (2026): Juli
Publisher : Program Studi Teknik Informatika, Fakultas Teknik dan Ilmu Komputer, Universitas Sains Al-Qur'an

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32699/biner.v5i2.11465

Program Makan Bergizi Gratis (MBG) merupakan kebijakan intervensi gizi nasional yang memicu beragam respons masyarakat di media sosial, khususnya YouTube. Penelitian ini bertujuan menganalisis sentimen publik terhadap program MBG, membandingkan kinerja Random Forest, Naive Bayes, dan Heuristic Pour, serta mengevaluasi pengaruh Synthetic Minority Oversampling Technique (SMOTE) dalam menangani ketidakseimbangan kelas. Sebanyak 2.278 komentar YouTube periode Agustus 2024–Januari 2025 diproses melalui tahapan cleaning, tokenizing, stopword removal, stemming, dan pembobotan TF-IDF. Evaluasi model dilakukan menggunakan 10-fold cross-validation berdasarkan confusion matrix, Precision, Recall, F1-score, dan Area Under Curve (AUC). Hasil penelitian menunjukkan bahwa Random Forest memberikan performa terbaik dengan akurasi 0,88 dan AUC 0,91. Penerapan SMOTE meningkatkan kemampuan model dalam mendeteksi sentimen positif sebagai kelas minoritas. Distribusi sentimen didominasi sentimen negatif lebih dari 87,5%. Penelitian ini memberikan kontribusi empiris terkait trade-off antara akurasi dan interpretabilitas dalam analisis sentimen berbahasa Indonesia.. Kebaruan penelitian ini terletak pada perbandingan komprehensif pendekatan machine learning dan rule-based dipadukan dengan SMOTE pada analisis sentimen komentar YouTube berbahasa Indonesia terkait kebijakan MBG. Penelitian ini memberikan bukti empiris mengenai efektivitas penanganan ketidakseimbangan kelas dan menjadi referensi dalam pengembangan analisis sentimen untuk evaluasi kebijakan publik

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