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All Journal International Journal of Electrical and Computer Engineering JURNAL SAINS PERTANIAN EQUATOR 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 Jurnal Kesehatan Vokasional Indonesian Journal of Artificial Intelligence and Data Mining Pendas : Jurnah Ilmiah Pendidikan Dasar 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 Journal of Applied Data Sciences Jurnal Pengabdian kepada Masyarakat Jurnal Kedaymas JURNAL ILMIAH GLOBAL EDUCATION International Journal Software Engineering and Computer Science (IJSECS) 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 Bookchapter Pendidikan Universitas Negeri Semarang ROUTERS: Jurnal Sistem dan Teknologi Informasi jabdimasunipem Jurnal Indonesia : Manajemen Informatika dan Komunikasi Jurnal Esensi Infokom : Jurnal Esensi Sistem Komputer dan Informasi
Sri Lestari
Institut Informatika Dan Bisnis Darmajaya
Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Nursing Physics Public Health Social Sciences Other

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OPTIMALISASI PENANGANAN SPARSITY MENGGUNAKAN RANDOM FOREST, DEEP LEANING, DAN HOT-DECK IMPUTATION Lestari, Sri; Satrio, Rafli Banu; Kurniawan, Hendra; Saleh, Sushanty
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 11, No 1 (2026)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v11i1.7692

Abstract

Sparsity data dalam sistem rekomendasi dapat menurunkan akurasi prediksi dan relevansi saran. Penelitian ini membandingkan tiga metode imputasi—Random Forest Imputation, Deep Learning-Based Imputa-tion, dan Hot-Deck Imputation—dengan evaluasi menggunakan RMSE pada berbagai tingkat sparsitas. Hasil menunjukkan bahwa Random Forest Imputation consistently menghasilkan RMSE terendah di semua kondisi. Pada sparsitas 20%, metode ini lebih unggul dibandingkan Deep Learning-Based Imputation dengan selisih hingga 0.443 dan Hot-Deck Imputation hingga 0.338. Perbedaan RMSE se-makin meningkat seiring bertambahnya sparsitas, dengan selisih terbesar pada sparsitas tertinggi masing-masing dataset. Secara kese-luruhan, Random Forest Imputation terbukti paling efektif dalam me-nangani sparsitas dan meningkatkan akurasi rekomendasi.
DEEP LEARNING SOLUTION FOR SPARSITY PROBLEM TO IMPROVE RECOMMENDATION QUALITY Lestari, Sri; Mariana, Tiwuk
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 11, No 1 (2026)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v11i1.7027

Abstract

Recommendation systems have become indispensable across various platforms due to their ability to enhance personalized services. However, these systems face a critical challenge known as sparsity. Sparsity occurs when there are numerous gaps in data, making user preferences unknown. This results in less relevant recommendations, reducing system effectiveness and diminishing user satisfaction. Moreover, it can lead to missed business opportunities. The purpose of this study is to address the sparsity problem using Deep Learning to enhance recommendation quality. The research stages include literature review (SLR), data collection from the Netflix Prize dataset obtained from kaggle.com, data preprocessing, Deep Learning implementation, testing, analysis, and conclusions. The stages of this study are conducted literature study (SLR), data collection, data preprocessing, Deep Learning implementation, testing and analysis, and conclusions. The method of this study is carried out data preprocessing and imputaion using several existing methods by using the Netflix Prize dataset, data taken from kaggle.com. The result of this study shows that the Deep Learning method is able to solve the sparsity problem to improve the quality of recommendations, because the experimental results states that the Root Mean Squared Error (RMSE) value is the smallest compared to the Matrix-Factorization, SVD, KNN and other methods.
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.
Analisis Kepuasan Implementasi Bot WhatsApp Pelayanan Permohonan Surat Akademik UNU Lampung Menggunakan Metode PIECES Muhammad Haris Hermanto; Hadi Nurma Dwi Saputra; Ableo Swares; Sri Lestari
JTRISTE Vol 13 No 1 (2026): JTRISTE
Publisher : STMIK KHARISMA Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55645/jtriste.v13i1.703

Abstract

Academic administration is a core service in higher education institutions that involves a high level of interaction with students, particularly in managing academic documents. Along with the increasing number of students and the demand for fast, accurate, and responsive services, Universitas Nahdlatul Ulama (UNU) Lampung developed a WhatsApp Bot for academic document request services based on Node.js without using the official WhatsApp API. However, the implementation of a non-official API-based chatbot requires a comprehensive readiness evaluation that considers not only technical aspects but also non-technical dimensions. This study aims to analyze user satisfaction and implementation readiness of the WhatsApp Bot for academic document services at UNU Lampung using the PIECES method. This research employs a descriptive quantitative approach, with data collected through observation and questionnaires. The questionnaire was designed based on the PIECES indicators, namely Performance, Information, Economics, Control, and Service, and involved 52 student respondents. The data were analyzed by calculating the average satisfaction score for each indicator. The results show that all PIECES indicators fall within the satisfied category, with average scores of 4.04 for Performance, 4.00 for Information, 4.11 for Economics, 4.08 for Control, and 3.99 for Service. The overall average satisfaction score of 4.04 indicates that the implementation of the WhatsApp Bot is satisfactory and feasible as an academic service system. The Economics indicator achieved the highest score due to significant time and cost efficiency benefits, while the Service indicator obtained the lowest score, mainly due to limitations in automated service interaction. Overall, the WhatsApp Bot improves the efficiency and effectiveness of academic document services and is suitable for further development.
Recommendation for Self-Help Housing Stimulus Assistance (BSPS) Recipient Using Multi-Criteria Decision Making Methods Deka Mario; Sri Lestari
PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Vol. 12 No. 2 (2024): September 2024
Publisher : LPPM Universitas Islam 45 Bekasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33558/piksel.v12i2.9584

Abstract

The Housing and Settlement Area Office aims to improve the accuracy and efficiency of selecting beneficiaries for the Self-Help Housing Assistance (BSPS) program by using the Weighted Product (WP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) methods. This study was conducted in Sungkai Tengah Subdistrict, North Lampung, involving six criteria: land ownership, employment, house condition, income, independence, and household size.The WP method is used to determine the weight of each criterion by calculating the product of the criteria values raised to the power of their respective weights, which helps in ranking the alternatives. TOPSIS is applied to identify the best alternative by comparing the geometric distance of each option to the ideal solution, which is either the maximum or minimum value of each criterion The results indicate that the WP and TOPSIS methods can provide more objective and transparent rankings. The application of these methods resulted in the highest ranking for ID V3 with a vector V score of 0.1317. The decision support system developed from this research is expected to assist the Housing and Settlement Area Office in distributing aid more accurately, fairly, and efficiently, thereby supporting the government's goals in improving housing quality and spatial planning
Sistem Pendukung Keputusan Penerimaan Bantuan PKH di Kelurahan Tanjung Sari I Komang Swandika; Achmadi Hudadin Albarqi; Chairani Fauzi; Sri Lestari
Jurnal Esensi Infokom : Jurnal Esensi Sistem Informasi dan Sistem Komputer Vol 8 No 1 (2024)
Publisher : Institut Bisnis Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55886/infokom.v8i1.832

Abstract

Berdasarkan temuan berbagai penelitian, terdapat banyak kesalahan dalam distribusi statistik Program Keluarga Harapan (PKH) yang tidak akurat. Hal serupa juga ditunjukkan oleh hasil survei yang dilakukan di Kelurahan Tanjung Sari , Buay Pemaca, Kab. Ogan Komering Ulu Selatan . Hal ini menunjukkan bahwa masih banyak masyarakat yang masih mempunyai klaim atas uang tersebut namun tidak menerimanya. Terutama jika sejumlah calon peserta berada dalam kondisi miskin atau kurang beruntung dan tingkat kelayakan mereka hampir sama.Penelitian Penerimaan Program Keluarga Harapan (PKH) dengan Memanfaatkan Metodologi Simple Additive Weighting (SAW) dan Weighted Product (WP) pada Sistem Pendukung Keputusan (SPK) PKH di Kelurahan Tanjung Sari, Buay Pemaca .Hasil pada penelitian disini menunjukkan bahwa meskipun terdapat perbedaan di antara masing-masing pendekatan, seperti yang ditunjukkan oleh perbandingan hasil pemeringkatan pada Tabel 9, terdapat kesamaan antara hasil dari Peringkat 1–12 dan perbedaan antara hasil dari Peringkat 13–27.Penelitian menyimpulkan bahwa di Kelurahan Tanjung Sari , Buay Pemaca, , metode Weight Product (WP) dapat direkomendasikan sebagai metode sistem pendukung keputusan penerimaan bantuan PKH. Hasil yang ditampilkan dalam penelitian ini mempunyai rentang nilai yang sangat sempit, menandakan bahwa keakuratan data telah teruji.
Klasifikasi Risiko Kredit Nasabah Menggunakan Algoritma Machine Learning dan Teknik Explainable AI Ismi Hayati Nabila; Sri Lestari
Jurnal Ilmiah Global Education Vol. 7 No. 2 (2026): JURNAL ILMIAH GLOBAL EDUCATION (In Press)
Publisher : LPPM Institut Pendidikan Nusantara Global

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55681/jige.v7i2.5941

Abstract

The banking sector faces a major challenge in the form of credit risk due to customers' inability to repay loans, which can threaten financial stability. Traditional methods are less effective at handling complex data and class imbalances, so a machine learning approach is needed to improve classification accuracy. This study compares the performance of the Naïve Bayes, AdaBoost, Random Forest, and XGBoost algorithms on the German Credit dataset (1000 entries, 70% low risk and 30% high risk) with SMOTE techniques to overcome class imbalances as well as SHAP as Explainable AI for model interpretability. Data processing is carried out using Python (Pandas, Scikit-learn, XGBoost, SHAP) in Google Colab, including preprocessing (handling missing values, encoding, scaling), 10-fold cross-validation evaluation, and SHAP analysis. The results showed that Random Forest achieved the best performance with an average accuracy of 87% (Std: 0.036), precision 0.88, recall 0.87, F1-score 0.87, and ROC-AUC 0.93, followed by XGBoost (85%, Std: 0.041). Naïve Bayes and AdaBoost only reached 81%. SHAP analysis revealed Credit Amount and Duration as the most influential features on high risk prediction (positive correlation ~0.62). The ensemble model excels in accuracy and stability, while the integration of SMOTE and SHAP improves minority class recall as well as transparency for banking decision-making. This study outperformed previous studies (71.33% in ANN and 83% in Random Forest) thanks to the combination of these techniques, supporting more accurate, ethical, and regulated credit risk management in financial institutions.
Peningkatan Akurasi Sistem Rekomendasi Film Menggunakan TF-IDF dan Cosine Similarity dengan Metode Hybrid Feature Engineering Aryono Prihandito; Sri Lestari; Fitria; Ketut Artaye
ROUTERS: Jurnal Sistem dan Teknologi Informasi Vol. 4 No. 2, Juli 2026 (In Progress)
Publisher : Program Studi Teknologi Rekayasa Internet, Politeknik Negeri Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25181/rt.v4i2.4929

Abstract

Content-based filtering movie recommendation systems are widely used to help users find relevant movies. However, most studies still rely on a single feature such as a synopsis, resulting in a less informative feature representation that impacts the quality of recommendations. This study aims to improve the accuracy of movie recommendation systems using TF-IDF and Cosine Similarity through the application of Hybrid Feature Engineering, which combines synopsis, genre, and keywords. Evaluation was conducted using the TMDb 5000 Movie Dataset with Precision, Recall, and F1-Score metrics in a Top-10 Recommendation scenario. The results showed that the hybrid model increased Precision from 0.7600 to 0.9600 (26.3%), Recall from 0.0048 to 0.0060 (25.0%), and F1-Score from 0.0095 to 0.0120 (26.3%) compared to the baseline model. The system was also successfully implemented as a Streamlit-based web application. These results indicate that combining multiple features through Hybrid Feature Engineering can produce a more informative feature representation and improve the quality of recommendations in content-based filtering systems
Development of an Early Warning System for Predicting Student Academic Failure Using PSO-Based Machine Learning Ni Made Novia Rezkianti; Sri Lestari
Indonesian Journal of Artificial Intelligence and Data Mining Vol. 9 No. 1 (2026): March 2026
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Student academic failure is a critical issue in higher education, as it affects graduation rates and the overall quality of an institution. Early identification of students at risk is essential to enable timely academic interventions. This study aims to develop a predictive model to identify students at risk of academic failure using machine learning techniques. The dataset used in this research was obtained from the UCI Machine Learning Repository and includes students’ demographic, socio-economic, and academic attributes. This study applies Particle Swarm Optimization integrated with Mutual Information (PSO-MI) as a feature selection method. It compares the performance of K-Nearest Neighbor (KNN) and Neural Network (NN) classification algorithms. The feature selection process identified 12 relevant features related to students' academic performance and administrative information. Model evaluation was conducted using two validation schemes: split validation with an 80:20 ratio and k-fold cross-validation, and performance was assessed using precision, recall, and F1 Score metrics. The experimental results show that the Neural Network model with PSO-MI-based feature selection consistently outperformed the KNN model under both validation schemes. In the cross-validation experiment, the Neural Network model achieved an accuracy of 0.91, a precision of 0.91, a recall of 0.89, and an F1-score of 0.90, indicating better performance in identifying students at risk of dropout. These findings demonstrate that integrating PSO-based feature selection with Neural Network classification offers a promising approach to predicting academic failure. The proposed framework can support the development of early warning systems to help educational institutions identify at-risk students and implement timely academic interventions
Prediction of land suitability for food crop types using classification algorithms Sri Lestari; Suci Mutiara
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 23, No 5: October 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v23i5.26826

Abstract

Decision-making in the selection of crop types is often conducted using conventional approaches. It is relying on limited experience and knowledge without considering the latest data or information. This approach has the loss of opportunities to use crop types. The crop types are more suited to environmental conditions and market demand, and it inhibits the application of innovation in agriculture. Therefore, the use of information technology becomes crucial to enhance accuracy in determining land suitability and crop selection. This study recommends the Random Forest algorithms and AdaBoost due to their excellent performance across all metrics (AUC, CA, F1, Precision, Recall) on various dataset sizes with scores above 0.9, so it is the solution to predict land suitability for specific crop types. Furthermore, it enables farmers to maximize land potential and achieve optimal yields.
Co-Authors Ableo Swares Achmadi Hudadin Albarqi Ade Moussadecq Adhani Windari, Adhani Adigue, Andrea P. Afifi, Sesaria Nisa Aisha Nurwanti, Lathifah Albarqi, Achmadi Hudadin Ali Muhdi Amalyanda Azhari Ananta, Rezky Anas Ikhsanudin Andien Amalia Anugerahwati, Zulfi Apipah, Nida Apri Triansyah Aprilia, Indri Mada ARI SUSANTO Aron Naldi Rirongga Arti, Dwi Windu Kinanti Aryono Prihandito Aswin Aswin Aswin Aswin Aswin Aswin Aziz, RZ. Abdul Bambang Priyono Basuki, Sucipto Billy Zia Napoleon Bayusunuputro Chairani Chairani Christian Petrus Silalahi Chyrine, Ervana Dadang Mulyadi Saleh Deka Mario Desya, Nabilah Dewi Tryanasari Diantoro, Wawan Dwi Ernawati Dwi Zulfita Eldina, Ratih Fadhilah, Isnaini Qoriatul Fatonah Fatonah Faturochman Faturochman Faurani Santi Singagerda Fauzi, Chairani Fayzhall, Miyv 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 Heny Setyawati Hutagalung, Dhaniel I Komang Swandika Indah Pratiwi Indianto S, Dimas Ismail , Ismail ismail, Rendy Ismi Hayati Nabila Iwan Stia Budi JAINURI, JAINURI Joko Triloka Julian Tohir, Oxana Kartika Sari Dewi Ketut Artaye Kusmawaty, Dewi Lailany, Afyra Ar’bah Ma'ruf, Singgih Yulizar Mahalul Azam Maharani, Herlina Maidaswar Maidaswar Mariana, Tiwuk Maulidi Maulidi Mega Fatimah Rosana Mieke Rahayu Miranda, Thesa Moelyono, Achmad Mohamad Fahmi Hafidz Muflikha, Ikha Muhammad Haris Hermanto Muhammad Redintan Justin Mukaromah, Hafsah Mutiara, Suci Nabil Ahyan Annakhief Ni Made Novia Rezkianti Ningsiah Nugroho, Anan Nurdiyanto, Heri Nurharsono, Tri Pangesti, Audilla Sekar Parulian, Evan Pasaribu, Ryan Lucky Bahara Pramesti, Shinta Dwi Surya Prasetio, Diki Bima pratama, rinaldi satria Putri Putri, Nasya Adelia Rafli Banu Satrio Rahmansyah, Ferdian Rahmawati, Dewi Cantika Regina Susanti, Lanny Rendi Saputra Ricko Irawan Rini Mutahar Rio Kurniawan Rionaldi Ali Rismada, Yessindah Citra Raya Riyanto Riyanto 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 Sahiroh, Eli Sakhroji, Sakhroji Salsabila, Alif Mazida Santosa, Bagus Sari, Kiki Yulia Satrio, Rafli Banu Sekar Dana Chiatra Setya Rahayu Sihotang, Hottua Silitonga, Nelson Simorangkir, Alexandro Pardamean Siswahyudianto Siti Maesaroh Siti Rahmawati Soedjatmiko Soedjatmiko Sriyanto Suci Fithriya Suhendro Yusuf Irianto Sukriyah, Sukriyah Sulyono, Sulyono Sumartha, Divaretta K. Sushanty Saleh Swandika, I Komang Syahfira, Welda Sylvia Sylvia Tomas, Mary Jane L. Toro, Robby tri utami, septiana Tumimbang, Wilmar Uichol Kim Vivi Rulviana Wafi, Ahmad Zein Al Wahib, Moh Widi Nugroho, Handoyo Widianti, Evani Putri William Jonathan, William WINANTI, WINANTI Winda Rika Lestari Wiyono, Nuri Wulandari, Hanny Y, M Ariza Eka Y. Suhendro Yan Aditiya Pratama Yufi Wiyos Rini Masykuroh Yuliani Setyaningsih Yulianti, Tantia Alif Yuliatun, Ismiyati Yulmaini Yulmaini Yuni Puspita Sari Yunita Wulandari Yusuf Irianto, Suhendro Zarnelly Zarnelly