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All Journal International Journal of Electrical and Computer Engineering Teknika Techno.Com: Jurnal Teknologi Informasi TELKOMNIKA (Telecommunication Computing Electronics and Control) PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic JSI: Jurnal Sistem Informasi (E-Journal) Jurnal Informatika Jurnal Informatika Proceeding International Conference on Information Technology and Business Sinergi JUITA : Jurnal Informatika International conference on Information Technology and Business (ICITB) Jurnal Teknologi Informasi dan Bisnis Pengabdian Masyarakat Darmajaya Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) International Journal of Artificial Intelligence Research Jurnal CoreIT Indonesian Journal of Artificial Intelligence and Data Mining JURNAL PENDIDIKAN TAMBUSAI Jurnal Informasi dan Komputer JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Aisyah Journal of Informatics and Electrical Engineering Jurnal Sains dan Teknologi JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Computer Networks, Architecture and High Performance Computing Yumary: Jurnal Pengabdian kepada Masyarakat Journal of Applied Data Sciences Jurnal Pengabdian kepada Masyarakat Jurnal Kedaymas JURNAL ILMIAH GLOBAL EDUCATION Jurnal Teknologi Sistem Informasi Jurnal MathEducation Nusantara Jurnal Publika Pengabdian Masyarakat Journal of Technology Research in Information System and Engineering Malcom: Indonesian Journal of Machine Learning and Computer Science ROUTERS: Jurnal Sistem dan Teknologi Informasi Jurnal Indonesia : Manajemen Informatika dan Komunikasi Khazanah Informatika : Jurnal Ilmu Komputer dan Informatika Jurnal Esensi Infokom : Jurnal Esensi Sistem Komputer dan Informasi
Sri Lestari
Institut Informatika Dan Bisnis Darmajaya
Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Physics Public Health Social Sciences Other

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Penguatan Kapasitas Masyarakat melalui Pelatihan Energi Surya dan Sistem Informasi Desa pada Program SERASI Yan Aditiya Pratama; Hovi Herawadi Sudibyo; Sri Lestari
Yumary: Jurnal Pengabdian kepada Masyarakat Vol 6 No 3 (2026): Maret
Publisher : Penerbit Goodwood

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35912/yumary.v6i3.6128

Abstract

Purpose: This community service study aims to enhance community capacity in managing eco-tourism and local economic activities through training-based technology adoption under the SERASI program in Desa Sidodadi. Research Methodology: The program was implemented in Desa Sidodadi, Pesawaran Regency, Indonesia, using a Participatory Action Research (PAR). Activities included socialization, training, mentoring, and evaluation. The main tools applied were Smart Eco-Lighting systems based on solar energy, a Village Information System website with an MSMEs e-catalog, and digital promotion platforms. Results: The results indicate improved community knowledge and practical skills in renewable energy utilization and digital management. Participants were able to operate and maintain Smart Eco-Lighting, manage village website content, and use digital media to promote eco-tourism and MSMEs products. The establishment of a local management team reflects increased community readiness and ownership. Conclusions: The results indicate that training-based community empowerment effectively supports sustainable eco-tourism and digital village development by strengthening technical, institutional, and human resource capacity, while fostering community participation, ownership, and long-term sustainability. Limitations: This study is limited by focusing on one village. Thus, it may affect the generalizability of the results to other contexts. Contributions: This study proposes a practical model for community-based training that integrates renewable energy and digital technology to support sustainable rural tourism and local economic development.
Carbon Emission Trends (1999–2022): Forecasting Association of Southeast Asian Nations (ASEAN)'s Future Using a Hybrid Approach to Support Zero-Emission Policies Muhammad Rama Dhika; Sri Lestari
Indonesian Journal of Artificial Intelligence and Data Mining Vol. 8 No. 3 (2025): November 2025
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

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Abstract

Carbon Dioxide (CO₂) emissions are a primary driver of global climate change, with the energy sector being the dominant contributor. Southeast Asia, experiencing rapid economic growth, faces significant increases in CO₂ emissions due to high energy consumption. This study proposes a hybrid Autoregressive Integrated Moving Average (ARIMA)-XGBoost approach to predict CO₂ emissions in Association of Southeast Asian Nations (ASEAN) countries from 2023 to 2035, overcoming limitations of traditional linear models by combining machine learning (XGBoost) and time-series analysis ARIMA. Results demonstrate high accuracy (R² = 0.98) with the identification of key factors, including Gross Domestic Product (GDP), population, and total greenhouse gas (GHG) emissions. For instance, Indonesia's emissions are predicted to rise from 841.84 MtCO₂ (2023) to 2197.36 MtCO₂ (2035), while Brunei's emissions decrease from 10.86 MtCO₂ to 9.57 MtCO₂. Residual analysis and k-fold cross-validation confirm model robustness. These findings underscore the need for differentiated policies, such as renewable energy transitions in high-growth emission countries (Indonesia, Philippines) and regulatory strengthening in stable-trend nations (Brunei, Laos). The study provides methodological contributions to data-driven emission forecasting and evidence-based policy recommendations for the Association of Southeast Asian Nations (ASEAN) climate change mitigation.
Advanced Machine Learning Implementation for Early Detection and Prediction of Alzheimer's Disease Christian Petrus Silalahi; Sri Lestari
Indonesian Journal of Artificial Intelligence and Data Mining Vol. 8 No. 3 (2025): November 2025
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

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Abstract

Early detection of Alzheimer's disease is essential for more effective patient care. This study explores the application of Machine Learning (ML) algorithms in detecting Alzheimer's disease by analyzing influential factors, such as demographic profile, medical history, and clinical examination results. Five ML methods, namely Deep Learning, Random Forest, Decision Tree, Naïve Bayes, and Logistic Regression, are used to classify Alzheimer's disease cases. In addition, the study used RFE and BPSO methods for feature selection with the aim of improving model performance. The evaluation was conducted using cross-fold validation and split-validation techniques, with performance measured in terms of accuracy, precision, recall, and F1-score. The results showed that the Random Forest algorithm combined with BPSO achieved the best performance, with 99% accuracy and high precision and recall values, surpassing other methods. These findings demonstrate that integrating feature selection significantly improves classification quality and confirms the practical potential of ML models as reliable tools for the early detection of Alzheimer's disease, thereby assisting clinicians in diagnostic decision-making and enhancing patient care.
SiMoI New Method to Solve the Sparsity Problem in Collaborative Filtering Hendra Kurniawan; Sri Lestari; Sushanty Saleh; Rafli Banu Satrio
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.v7i1.1015

Abstract

Sparsity data is a major challenge in collaborative recommendation systems, characterized by the predominance of missing values within the user-item matrix. When a substantial portion of data is unavailable, the estimation process becomes hindered, and prediction accuracy declines due to limited usable information. To address this issue, this study introduces a novel method called SiMoI (Similarity, Mode, and Minimum Imputation), which is adaptively designed to handle high levels of sparsity. The SiMoI method combines user similarity with imputation strategies based on mode and minimum values. By leveraging subsets of the most informative users and items, the method efficiently fills missing entries while maintaining prediction stability. Evaluation was conducted using both real and synthetic datasets with varying sizes and degrees of sparsity, including an extreme scenario with 93.7% missing data. Experimental results show that SiMoI consistently produces more accurate predictions than baseline methods. Under high-sparsity conditions, SiMoI achieved an RMSE as low as 0.823, outperforming KNNI (0.947) and MEAN (1.021). Moreover, SiMoI demonstrated resilience across different data scales and sparsity distributions, indicating its flexibility and scalability in diverse contexts. These findings suggest that SiMoI is an effective and stable approach for addressing sparsity and holds strong potential for implementation in user-based recommendation systems, particularly in real-world scenarios where data availability is frequently limited.
Optimization of Sentiment Analysis of Government Regulation in Lieu of Law on Job Creation Using KNN, Random Forest, and PSO Sri Lestari; Tupari; Yan Aditiya Pratama; Suhendro
Khazanah Informatika : Jurnal Ilmu Komputer dan Informatika Vol. 11 No. 1 (2025): April 2025
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/khif.v11i1.3197

Abstract

Twitter is one of the social media used by the public to convey their views regarding the government's policy of issuing a Government Regulations in Lieu of Laws (Bahasa: Peraturan Pemerintah Pengganti Undang-undang (Perpu)). The public's pros and cons of this policy are material for sentiment analysis. The purpose of this study was to analyze Twitter users' opinions regarding the Job Creation Perpu using the K-Nearest Neighbors (KNN), Random Forest (RF), and Particle Swarm Optimization (PSO) methods. The data was 3.128 tweets from Twitter social media users regarding the Government Regulation in Lieu of Law on Job Creation. Based on 3.128 data, 1.599 sentiments were positive, 1.473 sentiments were negative and 53 sentiments were neutral. The results showed that PSO feature optimized Twitter social media sentiment analysis against this regulation. KNN and RF algorithms for sentiment analysis was carried out before and after optimization with PSO. Experimental results using RapidMiner 9.10 showed that PSO feature succeeded in increasing classification accuracy in both algorithms. Before optimization, the KNN accuracy value reached 80.40%, then increased significantly to 85.23% after optimization with PSO was applied. Meanwhile, Random Forest accuracy value before optimization was 77.21% and increased to 80.53% after PSO was applied. This result indicated that the PSO-based KNN algorithm had better performance in conducting sentiment analysis of the Government Regulation in Lieu of Law on Job Creation on Twitter compared to the Random Forest algorithm in the context of this study. It concluded that Random Forest algorithm based on PSO is the best classifier for sentiment analysis and a potential and effective algorithm for classifying and analyzing sentiment on the same topic.
Optimasi Pemanfaatan Lahan Desa Melalui Skoring dan Pemilihan Jenis Tanaman yang Tepat I Komang Swandika; Sri Lestari
TEKNIKA Vol. 19 No. 2 (2025): Teknika Mei 2025
Publisher : Politeknik Negeri Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.15522544

Abstract

Pertumbuhan jumlah penduduk dan kebutuhan pangan yang meningkat menyebabkan tekanan terhadap ketersediaan lahan pertanian produktif. Salah satu solusi yang dapat dilakukan adalah optimalisasi pemanfaatan lahan tidur dengan tanaman yang sesuai berdasarkan kondisi agroklimat setempat. Penelitian ini bertujuan untuk mengembangkan sistem rekomendasi tanaman yang sesuai dengan karakteristik wilayah berdasarkan metode penilaian skoring. Penelitian dilakukan melalui beberapa tahapan, yaitu identifikasi parameter agroklimat, pengumpulan data spasial dan non-spasial, pemberian bobot nilai terhadap parameter-parameter tersebut, serta analisis kecocokan lahan. Metode skoring dilakukan dengan memberikan nilai pada masing-masing parameter seperti curah hujan, suhu, ketinggian, dan jenis tanah, yang kemudian dijumlahkan untuk menentukan jenis tanaman yang paling cocok. Hasil penelitian menunjukkan bahwa sistem yang dikembangkan mampu memberikan rekomendasi jenis tanaman yang sesuai untuk suatu wilayah tertentu, dengan akurasi kecocokan berdasarkan validasi lapangan mencapai 87%. Temuan ini diharapkan dapat mendukung pengambilan keputusan dalam perencanaan tata guna lahan dan peningkatan produktivitas pertanian di wilayah yang selama ini belum dimanfaatkan secara optimal.
RankPro-M Method to Alleviate the Sparsity Problem in Collaborative Filtering Sri Lestari; Yulmaini Yulmaini; Suhendro Yusuf Irianto; Hari Sabita
Journal of Applied Data Sciences Vol 7, No 2: May 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i2.1173

Abstract

The rapid shift from conventional commerce to online platforms has been driven by evolving consumer behavior that demands fast, accurate, and personalized services. Consequently, e-commerce has become a primary channel for product marketing and service delivery without temporal or spatial constraints. However, the continuous expansion of e-commerce platforms has led to a substantial increase in both the volume and diversity of available products, thereby complicating the task of delivering personalized recommendations aligned with user preferences. Recommender systems offer an effective solution to this challenge, with Collaborative Filtering (CF) being among the most widely adopted techniques. Despite its popularity, CF suffers from a critical limitation known as the data sparsity problem, which adversely affects recommendation accuracy and system reliability. This study proposes RankPro-M, a ranking-oriented imputation approach designed to mitigate the impact of sparsity in recommender systems. RankPro-M operates by identifying items with high rating frequency and imputing missing ratings using mode values as representations of dominant user preferences. The imputed rating matrix is subsequently processed through ranking aggregation mechanisms (Borda, Copeland, and WP-Rank) to generate item recommendations. Experimental results demonstrate that the application of RankPro-M consistently improves recommendation quality, as indicated by increased Normalized Discounted Cumulative Gain (NDCG) values across multiple evaluation scenarios. These findings confirm that RankPro-M effectively addresses data sparsity and enhances the performance of ranking-based recommender systems.
SiMoI New Method to Solve the Sparsity Problem in Collaborative Filtering Hendra Kurniawan; Sri Lestari; Sushanty Saleh; Rafli Banu Satrio
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.v7i1.1015

Abstract

Sparsity data is a major challenge in collaborative recommendation systems, characterized by the predominance of missing values within the user-item matrix. When a substantial portion of data is unavailable, the estimation process becomes hindered, and prediction accuracy declines due to limited usable information. To address this issue, this study introduces a novel method called SiMoI (Similarity, Mode, and Minimum Imputation), which is adaptively designed to handle high levels of sparsity. The SiMoI method combines user similarity with imputation strategies based on mode and minimum values. By leveraging subsets of the most informative users and items, the method efficiently fills missing entries while maintaining prediction stability. Evaluation was conducted using both real and synthetic datasets with varying sizes and degrees of sparsity, including an extreme scenario with 93.7% missing data. Experimental results show that SiMoI consistently produces more accurate predictions than baseline methods. Under high-sparsity conditions, SiMoI achieved an RMSE as low as 0.823, outperforming KNNI (0.947) and MEAN (1.021). Moreover, SiMoI demonstrated resilience across different data scales and sparsity distributions, indicating its flexibility and scalability in diverse contexts. These findings suggest that SiMoI is an effective and stable approach for addressing sparsity and holds strong potential for implementation in user-based recommendation systems, particularly in real-world scenarios where data availability is frequently limited.
Co-Authors Ableo Swares Achmadi Hudadin Albarqi Ade Moussadecq Adigue, Andrea P. Aisha Nurwanti, Lathifah Albarqi, Achmadi Hudadin Amalyanda Azhari Anas Ikhsanudin Andien Amalia Anugerahwati, Zulfi Apri Triansyah Aprilia, Indri Mada ARI SUSANTO Aron Naldi Rirongga Aryono Prihandito Aswin Aswin Aswin Aswin Aswin Aswin Aziz, RZ. Abdul Chairani Chairani Christian Petrus Silalahi Dadang Mulyadi Saleh Deka Mario Dewi Tryanasari Diantoro, Wawan Dwi Ernawati Fatonah Fatonah Faturochman Faturochman Faurani I Santi Singagerda Fauzi, Chairani Febri Arianto Febri Arianto Ferdiansyah, Mohamad Fida Chasanatun, Fida Fikri, Ruki Rizal Nul Firdaus Rosman, Firdaus Fitri Agustina Fitria Fitria - Fitria Fitria Frasatya, {Ariffinto Ginting, Aurora Riseria Br Hadi Nurma Dwi Saputra Hari Sabita Hariyanto Wibowo, Hariyanto Hartono Hartono Hary Sulistyo Hastuti, Zulia Hendra Darmawan Hendra Kurniawan Heny Kusuma Widyaningrum Hovi Herawadi Sudibyo I Komang Swandika Indah Pratiwi Indianto S, Dimas Ismail , Ismail ismail, Rendy Ismi Hayati Nabila Iwan Stia Budi Joko Triloka Julian Tohir, Oxana Kartika Sari Dewi Ketut Artaye Kusmawaty, Dewi Lailany, Afyra Ar’bah Ma'ruf, Singgih Yulizar Maidaswar Maidaswar Mariana, Tiwuk Mega Fatimah Rosana Mieke Rahayu Miranda, Thesa Moelyono, Achmad Mohamad Fahmi Hafidz Muflikha, Ikha Muhammad Haris Hermanto Muhammad Rama Dhika Muhammad Redintan Justin Mukaromah, Hafsah Mutiara, Suci Nabil Ahyan Annakhief Ni Made Novia Rezkianti Ningsiah Nurdiyanto, Heri Pangesti, Audilla Sekar pratama, rinaldi satria Putri Rafli Banu Satrio Rahmansyah, Ferdian Rahmawati, Dewi Cantika Regina Susanti, Lanny Rendi Saputra Rini Mutahar Rio Kurniawan Rionaldi Ali Rismada, Yessindah Citra Raya Rodiatun, Rodiatun Rofa, Laili Alnur Romadhona, Prima Juanita Rosmita Nuzuliana, Rosmita Ruki Rizal Nul Fikri Runi Amanda Amalia RZ Abdul Aziz Sabur, Ambuy Safira, Zahra Santosa, Bagus Sari, Kiki Yulia Satrio, Rafli Banu Sekar Dana Chiatra Siswahyudianto Siti Rahmawati Sriyanto Suci Fithriya Suhendro Suhendro Yusuf Irianto Sulyono, Sulyono Sushanty Saleh Swandika, I Komang Syahfira, Welda Sylvia Sylvia Tomas, Mary Jane L. Toro, Robby tri utami, septiana Tupari Uichol Kim Vivi Rulviana Wahib, Moh Widi Nugroho, Handoyo Widianti, Evani Putri William Jonathan, William Winda Rika Lestari Wulandari, Hanny Y, M Ariza Eka Y. Suhendro Yan Aditiya Pratama Yufi Wiyos Rini Masykuroh Yulianti, Tantia Alif Yuliatun, Ismiyati Yulmaini Yulmaini Yuni Puspita Sari Yunita Wulandari Yusuf Irianto, Suhendro Zarnelly Zarnelly