Article Per Year (5 Year)
p-Index From 2021 - 2026
7.807
P-Index
This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering Teknika Techno.Com: Jurnal Teknologi Informasi 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 Journal of Applied Data Sciences Jurnal Pengabdian kepada Masyarakat Jurnal Kedaymas 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 JURNAL PENGABDIAN KEPADA MASYARAKAT (ADI DHARMA) Bookchapter Pendidikan Universitas Negeri Semarang 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 Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Physics Public Health Social Sciences Other

Published : 49 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 49 Documents
Search

 1   2   3   4   5 
Advanced Machine Learning Implementation for Early Detection and Prediction of Alzheimer's Disease Silalahi, Christian Petrus; Lestari, Sri
Indonesian Journal of Artificial Intelligence and Data Mining Vol 8, No 3 (2025): November 2025
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v8i3.38004

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.
Sentiment Analysis of Skincare Products Using Lexicon and Multinomial Naive Bayes on The Sociolla Website Rahmansyah, Ferdian; Sriyanto, Sriyanto; Lestari, Sri; Irianto, Suhendro Yusuf
Journal of Computer Networks, Architecture and High Performance Computing Vol. 7 No. 4 (2025): Articles Research October 2025
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v7i4.7048

Abstract

Global warming has triggered extreme weather that negatively affects skin health, including damage, premature aging, and increased risk of skin cancer, prompting the use of skincare products. E-commerce platforms like Sociolla simplify skincare purchases, but the abundance of choices and varying skin reactions make product selection challenging. This study aims to assist consumers in making smarter purchase decisions by analyzing user reviews using sentiment analysis with a lexicon-based approach and the Multinomial Naive Bayes algorithm to classify reviews as positive or negative. The process includes data collection, text preprocessing, model development, and performance evaluation. The results show that this method achieved an accuracy of 80,64%, demonstrating its effectiveness in helping consumers filter reviews and select appropriate skincare products.
Implementasi Metode SAW pada Sistem Seleksi Siswa Baru Berbasis Web Nabil Ahyan Annakhief; Sri Lestari
Journal of Information System Research (JOSH) Vol 7 No 3 (2026): April 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v7i6.9352

Abstract

New student admission is a crucial process in educational institution management because it determines the quality of accepted students. The Mathlaul Anwar Foundation offers several selection pathways: scholarships, report card grades, achievement pathways, and transfer pathways. Currently, the selection process is still conducted manually, resulting in various problems such as delays in data processing, potential calculation errors, lack of objectivity, and low transparency of selection results. This research aims to develop a web-based New Student Selection System using the Simple Additive Weighting (SAW) method as a decision support system to assist in the ranking process and determine student graduation objectively and measurably. The research methods used include observation, interviews, and documentation. The system development utilizes the Waterfall model, which consists of the stages of needs analysis, design, implementation, testing, and maintenance. The implementation results show that the system is able to reduce the selection process time from an average of 5 days to 2 days (a time efficiency of 60%). The process of calculating grades and ranking, which was previously done manually for approximately 120 minutes for 100 applicants, can be accelerated to approximately 15 minutes using the system (an efficiency increase of 87.5%). System testing using the Black Box method on 20 test scenarios showed a 100% functional success rate according to user requirements. In addition, the results of the SAW method calculation validation showed 100% accuracy compared to manual calculations. Thus, the application of the SAW method in the web-based new student selection system has been proven to be able to increase the efficiency, accuracy, objectivity, and transparency of the selection process at the Mathlaul Anwar Foundation.
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.
Co-Authors Ableo Swares Achmadi Hudadin Albarqi Ade Moussadecq Adhani Windari, Adhani Adigue, Andrea P. Afifi, Sesaria Nisa Aisha Nurwanti, Lathifah Albarqi, Achmadi Hudadin Amalyanda Azhari Anas Ikhsanudin Andien Amalia Anugerahwati, Zulfi Apipah, Nida Apri Triansyah Aprilia, Indri Mada ARI SUSANTO Aron Naldi Rirongga Aswin Aswin Aswin Aswin Aswin Aswin Aziz, RZ. Abdul Bambang Priyono Chairani Chairani Dadang Mulyadi Saleh Deka Mario Dewi Tryanasari Diantoro, Wawan Dwi Ernawati Dwiyono Amir, Hendy Eko Ginanjar, Sunandie Eldina, Ratih Fadhilah, Isnaini Qoriatul Fatonah Fatonah Faturochman Faturochman Faurani Santi Singagerda Fauzi, Chairani Febri Arianto Ferdiansyah, Mohamad Fida Chasanatun, Fida Fikri, Ruki Rizal Nul Firdaus Rosman, Firdaus Fitri Agustina 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 I Komang Swandika Indah Pratiwi Indianto S, Dimas Ismail , Ismail ismail, Rendy Iwan Stia Budi Joko Triloka Julian Tohir, Oxana Kartika Sari Dewi Kusmawaty, Dewi Lailany, Afyra Ar’bah Ma'ruf, Singgih Yulizar Mahalul Azam Maidaswar Maidaswar Mariana, Tiwuk Mega Fatimah Rosana Mieke Rahayu Miranda, Thesa Moelyono, Achmad Mohamad Fahmi Hafidz Muflikha, Ikha Muhammad Haris Hermanto Muhammad Redintan Justin Mukaromah, Hafsah Nabil Ahyan Annakhief Ningsiah Nugroho, Anan Nurdiyanto, Heri Nurharsono, Tri Pangesti, Audilla Sekar pratama, rinaldi satria Putri Rahmansyah, Ferdian Rahmawati, Dewi Cantika Regina Susanti, Lanny Rendi Saputra Ricko Irawan 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 Sabur, Ambuy Safira, Zahra Salsabila, Alif Mazida Santosa, Bagus Sari, Elen Sari, Kiki Yulia Satrio, Rafli Banu Setya Rahayu Silalahi, Christian Petrus Siswahyudianto Siti Rahmawati Soedjatmiko Soedjatmiko Sriyanto Suci Fithriya Suhendro Suhendro Yusuf Irianto Sulyono, Sulyono Sumartha, Divaretta K. Sushanty Saleh Swandika, I Komang Syahfira, Welda Sylvia Sylvia Tomas, Mary Jane L. Toro, Robby tri utami, septiana Tupari Uichol Kim Vivi Rulviana Wafi, Ahmad Zein Al 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