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All Journal Journal of Intelligent Decision Support System (IDSS) Jurnal Mandiri IT Jurnal Pengabdian Masyarakat Nauli Nomico
Hidayati, Ajeng
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Computer Science & IT Economics, Econometrics & Finance Education Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Social Sciences

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Bank Kalbar's Educational Programs for MSME Revitalization and Community Economic Development Haryono, Didi; Somantri, Gumilar Rusliwa; Gafur, Hanif Saha; Rofii, Muhammad Syaroni; Hidayati, Ajeng
Nomico Vol. 1 No. 5 (2024): Nomico-June
Publisher : PT. Anagata Sembagi Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62872/sjpxb984

Abstract

Micro, Small, and Medium Enterprises (MSMEs) are crucial to Indonesia's economy, contributing approximately 60.5% to the Gross Domestic Product (GDP) and employing about 96.9% of the national workforce. Despite the large number of MSMEs and their potential GDP contribution, there is still room for improvement in exports. The government has taken various measures to support MSME development, including policies that encourage digital business growth and education-based training for MSMEs. In West Kalimantan Province (Kalbar), MSMEs play a vital role in regional economic development. Despite their potential, they face several challenges such as access to capital, financial management, and market access. The regional government and the Bank of West Kalimantan have collaborated to revitalize MSMEs post-COVID-19 through various programs, credit schemes, and educational initiatives or training sessions. This study employs both qualitative and quantitative methods with a case study approach. The research results indicate that MSMEs significantly contribute to West Kalimantan's economic development, despite being impacted by the pandemic. The internal and external revitalization strategies implemented by Bank Kalbar have assisted MSMEs in recovery and adapting to digital-based business models. The study concludes that MSMEs in West Kalimantan have the potential to continue growing and thriving as independent economic entities in a digital-based business environment. The revitalization of MSMEs is a result of collaboration between MSME actors, the government, and banking institutions to achieve regional economic stability and recovery.
Implementation of TOPSIS method in decision support system for used motorcycle purchase recommendation Putra, Muhammad Ridho Alghifari; Manurung, Jonson; Hidayati, Ajeng
Journal of Intelligent Decision Support System (IDSS) Vol 8 No 2 (2025): June: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/idss.v8i2.289

Abstract

The selection of used motorcycles involves evaluating multiple criteria, such as price, production year, transmission type, vehicle type, mileage, fuel consumption, and engine capacity. This complex decision-making process often leads buyers to rely on subjective judgments or third-party recommendations, which may result in suboptimal choices. To address this issue, this research develops a decision support system based on the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), a Multi-Criteria Decision Making (MCDM) method, which ranks alternatives based on their proximity to the ideal solution. The study introduces innovation by applying TOPSIS to the specific context of used motorcycle selection, providing a data-driven, objective approach in contrast to conventional methods. A quantitative approach was employed, with data collected from online marketplaces and authorized dealerships. The results indicate that the 2019 Honda Revo, priced at Rp. 8,600,000, is the most optimal choice, achieving the highest preference score of 0.862887804. The effectiveness of the TOPSIS method in structuring the selection process ensures a more systematic and accurate decision-making process. Furthermore, the study highlights the influence of key criteria, such as fuel efficiency and mileage, in determining the ranking of alternatives. Future research should focus on integrating additional factors, such as maintenance history and vehicle condition, and exploring the development of web-based or mobile platforms to improve real-world implementation and enhance user accessibility. This system contributes to smarter, more informed decision-making in the used vehicle market, offering a significant advancement over traditional selection methods.
Comparative study of machine learning algorithms for predicting drug induced autoimmunity using molecular descriptors Delfiero, Yusuf Rio; Hidayati, Ajeng; Saputra, Bagus Hendra
Jurnal Mandiri IT Vol. 14 No. 1 (2025): July: Computer Science and Field.
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mandiri.v14i1.436

Abstract

Drug induced autoimmunity (DIA) poses significant challenges in pharmaceutical development due to its complex immunological mechanisms and delayed clinical manifestations. This study proposes a comparative evaluation of three ensemble machine learning models CatBoost, XGBoost, and Gradient Boosting for predicting DIA using molecular descriptors. A curated dataset of drug compounds with known autoimmune outcomes was analyzed through a systematic workflow incorporating preprocessing, stratified sampling, and model evaluation using accuracy, F1 score, and ROC AUC. Results indicate that CatBoost achieved the highest ROC AUC, while XGBoost demonstrated superior balance between precision and recall, as reflected by its F1 score. Feature importance analysis using SHAP highlighted key molecular properties such as SlogP_VSA10 and fr_NH2 as major contributors to prediction outcomes. The study provides a reproducible and interpretable framework for early toxicity screening, offering valuable insights for data driven decision making in drug safety assessment.
Implementation of association method using fp-growth algorithm on sales transaction data at Koperasi Primer Pullahta Hankam Pusdatin KEMHAN RI Aulia, Regifia Ningrum Nur; Prabukusumo, M Azhar; Hidayati, Ajeng
Jurnal Mandiri IT Vol. 14 No. 1 (2025): July: Computer Science and Field.
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mandiri.v14i1.446

Abstract

The conventional recording of sales transaction data frequently results in inaccuracies and presents significant obstacles to comprehensive data analysis. This study was conducted at Primkop Pullahta Hankam Pusdatin Kemhan RI with the aim of generating a product list based on item categories that are most frequently purchased together. These item combinations are expected to assist the cooperative in optimizing sales performance. The research employed a data mining technique known as association rule mining, which is designed to identify and predict customer purchasing behavior through analysis of transaction patterns. The dataset used comprised sales transaction records collected between September and November 2024. The FP-Growth algorithm was selected for its efficiency in identifying frequent itemsets without candidate generation. This algorithm utilized minimum support and confidence thresholds to generate association rules. The modeling process produced five association rules, each meeting the criteria of a minimum support of 20% and a minimum confidence of 80%, indicating strong co-occurrence among specific product combinations. Functional testing using the blackbox method demonstrated that all implemented features performed in accordance with specified functional requirements. The findings offer valuable insights for cooperative management by enabling data-driven decision-making in inventory planning, promotional bundling, and strategic sales targeting. These implications underscore the practical contribution of the research in enhancing operational efficiency and sales strategy within the cooperative sector.
Web-based development of room management information system at Universitas Pertahanan using Rapid Application Development Anjani, Prasashti Alya; Saragih, Hondor; Hidayati, Ajeng; Anindito
Journal of Intelligent Decision Support System (IDSS) Vol 7 No 3 (2024): Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/idss.v7i3.254

Abstract

Unhan RI is an educational institution responsible for facilitating the continuity of student’s academic activites, including the scheduling process that managed by the department’s staff. The scheduling process requires components such as courses, lectures, time slots, and the classrooms. The number of available classrooms at Unhan RI is less than it need. Therefore, a proper scheduling system is necessary to manage scheduling and avoid conflicts between schedule. The development of information management system for administration’s process that are still done manually are needed in this digital era. Because the large and continuously growing amount of data is difficult to process manually. The development is using Rapid Application Development method. This method is chosen because of the requirement time for the developing is short.  By using the room management information system, the process of scheduling courses and managing rooms can be done easily. This system provides information of room availability and ongoing activites, helping to prevent scheduling conflicts.
Pengembangan Potensi Masyarakat dalam Bijak Memanfaatkan Kecerdasan Buatan (AI) untuk Masa Depan di SMAN 1 Plered Kabupaten Purwakarta Firdaus, Eryan Ahmad; Manurung, Jonson; Saragih, Hondor; Prabukusumo, Muhammad Azhar; Hidayati, Ajeng; Kartika, Amelia
Jurnal Pengabdian Masyarakat Nauli Vol. 5 No. 1 (2026): Agustus, Jurnal Pengabdian Masyarakat Nauli
Publisher : Marcha Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/nauli.v5i1.379

Abstract

Perkembangan Kecerdasan Buatan (Artificial Intelligence/AI) telah membawa perubahan signifikan dalam berbagai bidang, termasuk pendidikan. Di satu sisi, AI memberikan berbagai kemudahan dalam proses pembelajaran, peningkatan kreativitas, serta efisiensi dalam memperoleh dan mengolah informasi. Namun, pemanfaatan AI yang tidak bijak dapat menimbulkan berbagai dampak negatif, seperti ketergantungan teknologi, plagiarisme, serta ancaman terhadap privasi dan keamanan data. Oleh karena itu, diperlukan upaya peningkatan literasi digital dan pemahaman etika dalam penggunaan AI bagi generasi muda. Kegiatan Pengabdian kepada Masyarakat (PkM) ini dilaksanakan di SMAN 1 Plered Kabupaten Purwakarta dengan tema “Pengembangan Potensi Masyarakat dalam Bijak Memanfaatkan Kecerdasan Buatan (AI) untuk Masa Depan”. Kegiatan ini bertujuan untuk meningkatkan pemahaman siswa mengenai konsep dasar AI, manfaat dan risikonya, serta mendorong pemanfaatan AI secara etis dan bertanggung jawab. Metode pelaksanaan dilakukan melalui penyampaian materi, diskusi, dan sesi tanya jawab secara interaktif. Hasil kegiatan menunjukkan bahwa peserta memperoleh pemahaman yang lebih baik mengenai penggunaan AI sebagai alat pendukung pembelajaran dan pengembangan diri. Kegiatan ini diharapkan dapat membentuk generasi muda yang adaptif, kreatif, dan siap menghadapi transformasi digital di masa depan.
Co-Authors Anindito Anjani, Prasashti Alya Aulia, Regifia Ningrum Nur Delfiero, Yusuf Rio Eryan Ahmad Firdaus Gafur, Hanif Saha Gumilar Rusliwa Somantri Haryono, Didi Hondor Saragih Kartika, Amelia Manurung, Jonson Muhammad Syaroni Rofii Prabukusumo, M Azhar Prabukusumo, Muhammad Azhar Putra, Muhammad Ridho Alghifari Saputra, Bagus Hendra