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Sistem Pendukung Keputusan dengan Aplikasi
Published by Ali Institute of Research and Publication
ISSN : 28292820     EISSN : 28292189     DOI : https://doi.org/10.55537/spk
Core Subject : Science,
Decision Sciences, Operations Research & Management
Artikel yang diterbitkan dalam Sistem Pendukung Keputusan dengan Aplikasi adalah relevansinya dengan masalah teoretis dan teknis dalam mendukung pengambilan keputusan yang ditingkatkan. Naskah dapat diambil dari beragam metode dan metodologi, termasuk dari teori keputusan yang didukung komputer.
Arjuna Subject : Ilmu Pengambilan Keputusan (semua kategori) - Ilmu Pengambilan Keputusan (Lain-Lain)
Articles 48 Documents
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Prediksi Gap-Up Saham Berbasis Logistic Regression Menggunakan Indikator Teknikal Yuan Anisa; Muhammad Hafiz; Hadijah Hadijah; Abdul Gani
Sistem Pendukung Keputusan dengan Aplikasi Vol 4 No 2 (2025)
Publisher : Ali Institute or Research and Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55537/spk.v4i2.1334

Abstract

This research aims to construct and test a predictive model for the gap-up phenomenon in stocks on the Indonesia Stock Exchange (IDX), with a case study on PT Bank Mandiri Tbk. (BMRI) stock. The research uses a quantitative approach by applying a Binary Logistic Regression model to analyze 1,213 daily historical data points from January 1, 2019, to January 1, 2024.Five technical analysis-based independent variables—vol_spike, rsi, prev_return, macd, and stochastic—were used to predict the probability of a stock gap-up occurrence.The analysis results show that the model as a whole is statistically significant (LLR p-value < 0.05), with an LLR p-value of (8.544e-11) and a Pseudo R-squared of 0.03389. Of the five variables, stochastic, macd, and prev_return were identified as significant predictors. Specifically, a high Stochastic Oscillator value has a strong positive influence on the probability of a gap-up. On the other hand, Moving Average Convergence Divergence (MACD) and Previous Return show a significant negative influence.These findings provide empirical evidence that a combination of technical indicators can be used to model and predict stock price movements at market opening. The implications of this research offer valuable insight for investors who rely on technical analysis as a basis for decision-making.
Decision Support System for Prioritizing Economic Sectors for Regional Investment Using AHP-TOPSIS Nurmaliana Pohan; Pradani Ayu Widya Purnama; Nurfa Rahma Julita; Hana Afifah
Sistem Pendukung Keputusan dengan Aplikasi Vol 5 No 2 (2026)
Publisher : Ali Institute or Research and Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55537/spk.v5i2.1709

Abstract

The volatility of quarterly Gross Regional Domestic Product (GRDP) growth across economic sectors poses a significant challenge for regional governments in determining investment priorities. This study develops a decision support system (DSS) to rank investment priorities among economic sectors in West Sumatra Province using a hybrid Analytic Hierarchy Process–Technique for Order Preference by Similarity to Ideal Solution (AHP-TOPSIS) method. Five criteria were extracted from three dimensions of GRDP growth rates, namely C-to-C, Q-to-Q, and Y-on-Y, for 2024–2025: average C-to-C growth, average Y-on-Y growth, Q-to-Q stability (the inverse of standard deviation), latest quarter growth (Q4 2025), and positive consistency (the number of quarters with growth > 0). Criteria weights were determined through an AHP pairwise comparison questionnaire completed by regional economic experts, resulting in a consistency ratio (CR) of 0.015. The TOPSIS method was then applied to rank 17 economic sectors. The results show that the top five investment priorities are Government Administration, Defense, and Mandatory Social Security (O) = 0.711; Health Services and Social Activities (Q) = 0.671; Financial Services and Insurance (K) = 0.633; Other Services (R) = 0.594; and Real Estate (L) = 0.573. The system was implemented as an interactive dashboard using Python Streamlit. The ranking results are consistent with regional development conditions, as validated by experts. This DSS provides an objective, data-driven tool for regional investment policymaking.
Decision Support System Model for Determining Priority Criteria for Quality of DANA Application Services Using the AHP Method Riko Amanah Gusni; Rieska Rahayu Ayuningsih; Rahmat Hidayat
Sistem Pendukung Keputusan dengan Aplikasi Vol 5 No 1 (2026)
Publisher : Ali Institute or Research and Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55537/spk.v5i1.1570

Abstract

Amid the rapid adoption of financial technology in Indonesia, user satisfaction has become a critical factor in sustaining digital wallet services. This study aims to analyze and determine the priority of service quality criteria that most significantly influence user satisfaction with the DANA application, while also developing a Decision Support System (DSS) model based on the Analytical Hierarchy Process (AHP) method to assist developers and product management in establishing service improvement priorities. This study employs a descriptive quantitative approach involving 152 students from the Information Systems Study Program at Universitas Nusa Putra, selected using a purposive sampling technique. Primary data were collected through a pairwise comparison questionnaire to assess four main dimensions of service quality: Security, Ease of Use, Performance, and Features. The AHP method was applied to generate priority weights for each criterion and to evaluate the consistency of respondents’ judgments. The analysis results indicate that Security is the top priority with a weight of 44.8%, followed by Ease of Use (25.1%), Performance (17.9%), and Features (12.2%). The Consistency Ratio (CR) value of 0.076 (< 0.1) confirms that the respondents’ judgments are consistent and valid. These findings demonstrate that for users with high technological literacy, data protection and usability are prioritized over feature completeness. The proposed DSS model provides a systematic decision-making framework for developers to focus on strengthening cybersecurity measures and simplifying the user interface to maintain user trust and loyalty.
Decision Support System for Regional Planning of Horticultural Commodities Using the CODAS Method Based on a Case Study Indra Irawan; Dio Arif Hanafi Harianja; Armansyah Putra Siregar; Muhammad Angga Septiawan; M Rizky Ramadhan; Mutammim Zisdian
Sistem Pendukung Keputusan dengan Aplikasi Vol 5 No 1 (2026)
Publisher : Ali Institute or Research and Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55537/spk.v5i1.1575

Abstract

This study aims to determine the priority of regional development for horticultural commodities. The planning and selection process involves various criteria, including productivity, land area, selling price, market demand, resistance to pests and climate, production costs, and government support; therefore, a systematic and objective approach is required. This study employs the Combinative Distance-Based Assessment (CODAS) method, a Multi-Criteria Decision Making (MCDM) approach that evaluates alternatives based on their Euclidean distance from the negative-ideal solution. The data used are secondary data obtained from the Department of Food Security, Food Crops, and Horticulture of North Sumatra Province and the Central Statistics Agency (BPS), complemented by literature review, observation, and interviews, and then analyzed quantitatively. The results show that bananas rank first with a Euclidean distance value of 0.209949, followed by cayenne pepper with 0.165406 and papaya with 0.161221. Furthermore, red chili (0.156669) and shallots (0.126977) also exhibit relatively high values compared to other commodities. A higher Euclidean distance indicates greater potential for development in regional horticultural planning. This study provides a priority ranking of commodities that can serve as a basis for decision-making in regional horticultural development planning in North Sumatra Province.
Sustainable Chicken Waste Product Selection Using a Hybrid CRITIC–MARCOS-Based Decision Support System Nurmaliana Pohan; Aditya Widodo; Arlan Tri Handika; Cindy Afriana Jambak; Hari Prayudha; Rayhan Atricha Rambe
Sistem Pendukung Keputusan dengan Aplikasi Vol 5 No 1 (2026)
Publisher : Ali Institute or Research and Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55537/spk.v5i1.1576

Abstract

The growth of the poultry industry has led to an increase in chicken waste volume, which may cause environmental and health problems if not properly managed. However, chicken waste also has the potential to be processed into value-added products that support sustainable agriculture and a circular economy. This study aims to develop an objective and systematic decision support system to determine the most sustainable chicken waste product based on economic efficiency and environmental impact within the operational context of the Agricultural Modernization and Assembly Agency (BRMP) of North Sumatra. A hybrid Multi-Criteria Decision Making (MCDM) approach was applied by integrating the Criteria Importance Through Intercriteria Correlation (CRITIC) method and the Measurement of Alternatives and Ranking according to Compromise Solution (MARCOS) method. The CRITIC method determines criteria weights objectively based on data variability and inter-criteria correlation, while the MARCOS method ranks alternatives based on utility values relative to ideal and anti-ideal solutions. Fifteen chicken waste product alternatives were evaluated using seven economic, technical, and environmental criteria. The results show that chicken manure compost is the best alternative, with the highest utility value (Kᵢ > 1), indicating superior performance in terms of production cost, market feasibility, environmental impact, and raw material availability. Sensitivity analysis confirms that the ranking results remain stable despite changes in criteria weights. This study contributes to the development of hybrid MCDM methods for agricultural waste management and provides a transparent and reliable decision-making framework for policymakers.
Optimization of Laying Duck Feed Combinations Using the COCOSO Method Harlan Kurnia AR; Raditya Abdillah Putra; Dwi Andini; Nazwa Chairunnisa Hamid Siagian; Muhammad Alfaridho; Hazlah Aqillah
Sistem Pendukung Keputusan dengan Aplikasi Vol 5 No 1 (2026)
Publisher : Ali Institute or Research and Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55537/spk.v5i1.1577

Abstract

The productivity of laying ducks is highly influenced by feed composition and nutrient quality. Inappropriate feed combinations may reduce production performance and increase operational costs. This study aims to determine the optimal feed combination for laying ducks using the Combined Compromise Solution (COCOSO) method by considering nutritional, production, and economic criteria. This study evaluates 15 alternative feed combinations consisting of commercial pellets and supplementary ingredients such as fine bran, ground corn, soybean meal, mineral mix, vitamin mix, and fish meal. Seven criteria were used, including protein, calcium, feed cost, egg production (HDP), health, palatability, and Feed Conversion Ratio (FCR), which were classified into benefit and cost attributes. The analysis process includes decision matrix construction, normalization, criteria weighting, and ranking using three COCOSO aggregation strategies to obtain compromise scores. The results indicate that differences in feed performance are clearly identified through multi-criteria evaluation. The best alternative achieved the highest COCOSO score of 0,940, indicating the most optimal balance between nutritional quality, production performance, feed efficiency, and cost. This study concludes that the COCOSO-based Multi-Criteria Decision Making (MCDM) approach is effective for supporting decision-making in livestock nutrition. The proposed model provides a structured, objective, and practical tool for farmers and practitioners to optimize feed strategies and improve production efficiency.
A CRITIC–CoCoSo-Based Decision Support Model for Coffee Bean Quality Evaluation in Coffee Beverage Production Yustria Handika Siregar; Muhammad Eka; Asri Akmaliyah Syahfitri; Dini Alilmi; Dwi Hafizah Akbar; Nurhaliza Febryani; Pira Safitri
Sistem Pendukung Keputusan dengan Aplikasi Vol 5 No 1 (2026)
Publisher : Ali Institute or Research and Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55537/spk.v5i1.1578

Abstract

Consistency in coffee bean quality is essential for maintaining flavor stability and production efficiency in coffee-based beverages. However, quality evaluation in small-scale coffee industries is often subjective and lacks a structured decision support system. This study aims to develop a decision support model for evaluating coffee bean quality using a Multi-Criteria Decision Making (MCDM) approach by integrating the CRITIC (Criteria Importance Through Intercriteria Correlation) and CoCoSo (Combined Compromise Solution) methods. The CRITIC method is employed to determine objective criterion weights based on data variability and inter-criteria correlations, while the CoCoSo method is used to rank alternatives. A total of 15 coffee bean alternatives were evaluated using seven criteria: price, availability, aroma, taste, color, texture, and caffeine content. The weighting results indicate that color (0.215) and price (0.191) are the most influential criteria in the evaluation process. The ranking results show that alternative A9 achieved the highest preference score (K = 4.1634), followed by A7 (K = 3.5747) and A6 (K = 3.4920). These results demonstrate that coffee beans with strong performance across sensory and physical attributes tend to achieve higher rankings. The proposed CRITIC–CoCoSo model provides a systematic, objective, and practical decision support tool that can assist small to medium-scale coffee industries in selecting high-quality raw materials, improving product consistency, and enhancing production efficiency.
Data Poisoning, Data Drift, and Data Integrity in Supply Chain Systems: Emerging Threats to AI Governance Umamaheswari Shanmugam; Mohan Kumar Rajendran; Natarajan Jawahar; Veera Venkata Satyanarayana Reddy Karri
Sistem Pendukung Keputusan dengan Aplikasi Vol 5 No 1 (2026)
Publisher : Ali Institute or Research and Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55537/spk.v5i1.1601

Abstract

Artificial intelligence (AI) plays a critical role in supply chain systems by enabling predictive analytics and data-driven decision-making. However, increasing reliance on AI exposes systems to significant data-related vulnerabilities that may compromise reliability and trust. This study investigates three major threats to AI governance: data poisoning, data drift, and data integrity. Using a qualitative literature-based analysis supported by synthesized empirical evidence, this study evaluates the impact of these threats on model performance and operational outcomes. The results show that data poisoning can significantly reduce model accuracy (from approximately 95% to below 75%) and introduce bias, while data drift leads to gradual performance degradation over time due to changing data distributions. In addition, data integrity issues—such as incomplete, corrupted, or unauthorized data—undermine decision reliability and amplify the effects of poisoning and drift. To address these challenges, the study proposes a multi-layered AI governance framework integrating technical safeguards (e.g., adversarial detection and continuous monitoring), organizational controls, and policy-level compliance mechanisms. The findings provide practical insights for improving AI robustness, operational resilience, and trust in supply chain environments, contributing to the development of effective and responsible AI governance.

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