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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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Fuzzy Preference Relations-Based AHP for Multi-Criteria Supplier Segmentation Nurdiyanto, Heri; Fauzi, Chairani; Lestari, Sri
International Journal of Artificial Intelligence Research Vol 8, No 1 (2024): June 2024
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v7i1.1.1103

Abstract

Supplier segmentation is a strategic activity for businesses. It involves dividing suppliers into distinct categories and managing them differently. Various supplier typologies based on different dimensions and factors are available in the existing literature. By highlighting two main characteristics the skills and the desire of suppliers to work with a specific company this article integrates many typologies. Almost all of the supplier segmentation criteria stated in the literature are covered by these dimensions. These dimensions can be defined utilizing a multi-criteria decision-making process for each specific case. To account for the inherent ambiguities and uncertainties in human judgment, a fuzzy Analytic Hierarchy Process (AHP) is suggested as part of the technique. This approach makes use of fuzzy preference relations. A broiler firm uses the suggested process to divide up its suppliers. A categorization of vendors according to two aggregated criteria is the end outcome. Lastly, we offer some suggestions for future research, draw some conclusions, and talk about some techniques to address distinct sectors
Implementasi K-Means Clustering Pada Big Data Di Sistem Rekomendasi Film Widianti, Evani Putri; Lestari, Sri
Jurnal Pendidikan Tambusai Vol. 8 No. 2 (2024)
Publisher : LPPM Universitas Pahlawan Tuanku Tambusai, Riau, Indonesia

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

Abstract

Perkembangan data yang sangat pesat ini, bisa juga disebut dengan ledakan data. Ledakan data terjadi karena kumpulan data yang besar dan terus berkembang setiap waktu atau biasa dikenal dengan big data. Ledakan data ini berdampak pada infrastruktur teknologi telekomunikasi, kecepatan akses dan volume data. Selain itu, dampaknya akan terasa sulit untuk menentukan data yang dibutuhkan, berbagi data, dan menjaga data tetap konsisten Skalabilitas adalah kemampuan suatu sistem untuk terus berfungsi secara efisien dan efektif ketika dihadapkan dengan peningkatan ukuran dataset dan kompleksitas algoritma. Dalam konteks sistem rekomendasi film menggunakan K-Means Clustering, tantangan skalabilitas mencakup beberapa aspek penting yang perlu dipahami. Dalam konteks big data, waktu eksekusi yang lama dapat mengurangi responsivitas sistem dan membuat pengguna tidak sabar dalam menerima rekomendasi. Kedua, kompleksitas algoritma k-means clustering juga merupakan faktor kunci dalam menilai skalabilitas sistem rekomendasi Semakin tinggi dimensi data dan jumlah atribut yang diperhitungkan dalam proses pengelompokan, semakin rumit perhitungan dan analisis yang diperlukan. Penerapan K-Means Clustering pada Big Data dalam sistem rekomendasi film berdasarkan usia dan rating menghadapi beberapa permasalahan, yaitu banyaknya data film, sehingga terdapat kendala dalam pengelolaan dan klasifikasi big data (skalabilitas) untuk menentukan rekomendasi film.
PREDICTION OF ANEMIA USING THE PARTICLE SWARM OPTIMIZATION (PSO) AND NAÏVE BAYES ALGORITHM tri utami, septiana; Sriyanto, Sriyanto; Lestari, sri; Widi Nugroho, Handoyo; zarnelly, zarnelly
Jurnal CoreIT: Jurnal Hasil Penelitian Ilmu Komputer dan Teknologi Informasi Vol 10, No 1 (2024): June 2024
Publisher : Fakultas Sains dan Teknologi, Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/coreit.v10i1.28428

Abstract

Purpose: Anemia is a nutritional disorder that is still often found in Indonesia. The main risk factors for iron deficiency anemia are low iron intake, poor iron absorption, and periods of life when the need for iron is high such as during growth, pregnancy, and breastfeeding. Anemia can generally occur in pregnant women, teenagers, the elderly and even babies who have anemia.Methods/Study design/approach: This research uses the Naive Bayes and PSO algorithms, and the dataset used comes from the kaggel.com Anemia dataset. The number of data records is 1421 data consisting of 5 attributes and 1 label. This data set is used to predict whether a patient is likely to suffer from anemia.Result/Findings: Based on the results of testing the Naïve Bayes and PSO algorithm models which were carried out through confusion matrix evaluation, it was proven that the tests carried out by the Naïve Bayes algorithm were 93.88% and the tests carried out with Naïve Bayes and PSO had a high accuracy value, namely 94.02%.Novelty/Originality/Value: The purpose of selecting information acquisition features is to select features or attributes that significantly influence anemia. Keywords: Prediction, Anemia, Naive Bayes, Particle Swarm Optimization (PSO)
Analisis Dan Implementasi Sistem Pendiagnosa Penyakit Tanaman Karet Menggunakan Metode Case Based Reasoning Rismada, Yessindah Citra Raya; Pratiwi, Indah; Lestari, Sri
TEKNIKA Vol. 18 No. 1 (2024): Teknika Januari - Juni 2024
Publisher : Politeknik Negeri Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.11003509

Abstract

Tanaman karet memegang peran penting dalam pertanian Indonesia, namun rentan terhadap serangan penyakit yang dapat menimbulkan dampak negatif yang signifikan. Untuk mengatasi masalah ini, telah dikembangkan sebuah sistem komputer cerdas yang menggunakan metode Case-Based Reasoning untuk mendiagnosis penyakit pada tanaman karet. Metode ini mempertimbangkan kesamaan antara kasus yang sedang dihadapi dengan kasus-kasus sebelumnya, memungkinkan sistem untuk mengidentifikasi gejala penyakit, mengetahui penyebabnya, dan menyediakan cara pengendaliannya. Penerapan sistem pakar dengan metode Case-Based Reasoning dianggap sebagai solusi optimal dalam mengidentifikasi masalah penyakit pada tanaman karet. Dengan mengintegrasikan data kasus, representasi dalam basis kasus, dan menggunakan algoritma CBR, sistem ini dapat memberikan diagnosa yang cepat dan akurat berdasarkan kesamaan dengan kasus-kasus sebelumnya. Hal ini diharapkan dapat meningkatkan efisiensi dalam diagnosis dan mengurangi kerugian yang ditimbulkan oleh penyakit tanaman karet. Hasil evaluasi menunjukkan tingkat akurasi yang memuaskan, mendukung efektivitas sistem dalam membantu petani mengatasi masalah penyakit tanaman karet. Uji coba perhitungan manual pada penelitian ini juga menghasilkan hasil yang konsisten dengan perhitungan pada sistem, memverifikasi keefektifan metode yang digunakan. Dengan demikian, sistem pendukung keputusan menggunakan metode Case Based Reasoning ini dapat menjadi alat yang berguna bagi petani dalam mengatasi masalah penyakit pada tanaman karet, meningkatkan produktivitas dan hasil panen mereka.
Penerapan Metode Case Based Reasoning Untuk Diagnosa Penyakit Pada Sapi Syahfira, Welda; Aprilia, Indri Mada; Lestari, Sri
TEKNIKA Vol. 18 No. 1 (2024): Teknika Januari - Juni 2024
Publisher : Politeknik Negeri Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.10881655

Abstract

Sektor peternakan, khususnya peternakan sapi, memainkan peran penting dalam mendukung ketahanan pangan dan ekonomi di Indonesia, salah satu negara agraris terbesar di dunia. Namun, tantangan seperti perubahan iklim, ketersediaan pakan, dan ancaman penyakit menimbulkan resiko yang signifikan bagi peternakan sapi. Identifikasi penyakit dengan cepat dan akurat penting untuk mengurangi risiko ini dan meminimalkan kerugian ekonomi bagi peternak. Penelitian ini berfokus pada pengembangan dan implementasi metode Case-Based Reasoning (CBR) sebagai sistem pakar untuk membantu peternak dalam mendiagnosa penyakit sapi secara efektif. Penelitian ini dilakukan dengan mengumpulkan data kasus penyakit dari berbagai sumber. Algoritma retrieval CBR digunakan untuk mencari kesamaan antara gejala penyakit yang muncul pada sapi yang sedang didiagnosa dengan kasus-kasus yang telah terdokumentasi sebelumnya. Sistem ini dirancang untuk memberikan rekomendasi diagnosa yang cepat dan akurat berdasarkan pola-pola yang teridentifikasi dari kasus-kasus penyakita yang sebelumnya telah ada. Berdasarkan perhitungan manual dan program komputer yang telah dibuat, percobaan kasus analisa penyakit hewan ternak sapi menghasilkan hasil yang sama yaitu dari kasus menghasilkan akurasi penyakit 94% dengan diagnose penyakit MASTITIS/Radang Ambing. Jadi dapat disimpulkan penelitian diagnose penyakit pada hewan ternak sapi memiliki akurasi 99,99%.
Penerapan Metode Case Based Reasioning Diagnosa Penyakit Ringworm Pada Kucing Wulandari, Yunita; Lestari, Sri
TEKNIKA Vol. 18 No. 1 (2024): Teknika Januari - Juni 2024
Publisher : Politeknik Negeri Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.11075796

Abstract

Kucing merupakan hewan peliharaan yang banyak digemari oleh masyarakat Indonesia. Bagi para pecinta kucing, keterbatasan jumlah pakar yaitu dalam hal ini dokter hewan, sering menjadi masalah bagi yang memelihara kucing di rumah dan ingin menjaga kesehatan kucing peliharaannya. Untuk mengatasi masalah ini maka dibuatlah sistem pakar untuk diagnosa penyakit kucing menggunakan metode CaseBased untuk menganalisis kasus penyakit ring worm. Penelitian ini menghasilkan sebuah sistem pakar diagnosa penyakit kucing dengan similarity 0,8666 atau persentasi 86,66%, sehingga dapat nilai keyakinan bahwa kasus baru ini terdiagnosa penyakit Ring Worm parah dengan nilai diagnose 86,66%.
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.
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