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All Journal International Journal of Electrical and Computer Engineering IJCCS (Indonesian Journal of Computing and Cybernetics Systems) TEKNIK INFORMATIKA Jurnal Informatika Dinamik Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Jurnal Ilmu Komputer dan Informasi AMIKOM ICT AWARD 2010 Techno.Com: Jurnal Teknologi Informasi Jurnal Simetris Jurnal Buana Informatika Jurnal Sarjana Teknik Informatika Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Dinamika Informatika CommIT (Communication & Information Technology) Explore: Jurnal Sistem Informasi dan Telematika (Telekomunikasi, Multimedia dan Informatika) Jurnal technoscientia Jurnal Teknologi Jurnal Pseudocode Jurnal Teknologi Informasi dan Ilmu Komputer Telematika Jurnal Edukasi dan Penelitian Informatika (JEPIN) Techno (Jurnal Fakultas Teknik, Universitas Muhammadiyah Purwokerto) JUITA : Jurnal Informatika Proceedings Konferensi Nasional Sistem dan Informatika (KNS&I) Seminar Nasional Informatika (SEMNASIF) CESS (Journal of Computer Engineering, System and Science) Proceeding SENDI_U Jurnal IPTEK-KOM (Jurnal Ilmu Pengetahuan dan Teknologi Komunikasi) Jurnal Inspiration KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) Proceeding of the Electrical Engineering Computer Science and Informatics PROtek : Jurnal Ilmiah Teknik Elektro Sistemasi: Jurnal Sistem Informasi JOIV : International Journal on Informatics Visualization Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) International Journal of Artificial Intelligence Research Creative Information Technology Journal SISFOTENIKA IJCIT (Indonesian Journal on Computer and Information Technology) Jurnal Ilmiah Universitas Batanghari Jambi INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi JIKO (Jurnal Informatika dan Komputer) JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Pilar Nusa Mandiri Journal of Electrical Technology Syntax Literate: Jurnal Ilmiah Indonesia Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control CogITo Smart Journal InComTech: Jurnal Telekomunikasi dan Komputer Insect (Informatics and Security) : Jurnal Teknik Informatika Jurnal Eksplora Informatika JITK (Jurnal Ilmu Pengetahuan dan Komputer) JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Komtika (Komputasi dan Informatika) JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Jurnal Informatika Universitas Pamulang Applied Information System and Management Jurnal Sinergitas PkM & CSR Jurnal Sisfokom (Sistem Informasi dan Komputer) ILKOM Jurnal Ilmiah RESEARCH : Computer, Information System & Technology Management INTECOMS: Journal of Information Technology and Computer Science JurTI (JURNAL TEKNOLOGI INFORMASI) Angkasa: Jurnal Ilmiah Bidang Teknologi Jiko (Jurnal Informatika dan komputer) CARADDE: Jurnal Pengabdian Kepada Masyarakat CYBERNETICS Digital Zone: Jurnal Teknologi Informasi dan Komunikasi JURIKOM (Jurnal Riset Komputer) Journal on Education JURTEKSI Jurnal Informasi dan Komputer Multitek Indonesia : Jurnal Ilmiah Jurnal Manajemen Informatika Jambura Journal of Informatics JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) EXPLORE ComTech: Computer, Mathematics and Engineering Applications CSRID (Computer Science Research and Its Development Journal) Jurnal Ilmiah Sinus Informasi Interaktif Majalah Ilmiah Bahari Jogja CCIT (Creative Communication and Innovative Technology) Journal EDUMATIC: Jurnal Pendidikan Informatika Jurnal Abdimas PHB : Jurnal Pengabdian Masyarakat Progresif Humanis Brainstorming M A T H L I N E : Jurnal Matematika dan Pendidikan Matematika TAFAQQUH: Jurnal Hukum Ekonomi Syariah Dan Ahwal Syahsiyah Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Technologia: Jurnal Ilmiah JURNAL TAHURI SENSITEK E-JURNAL JUSITI : Jurnal Sistem Informasi dan Teknologi Informasi Aisyah Journal of Informatics and Electrical Engineering Jurnal Manajemen Informatika dan Sistem Informasi Journal of Information Systems and Informatics TAJDID KURVATEK Jurnal Teknologi Informasi : Jurnal Keilmuan dan Aplikasi Bidang Teknik Informatika Jurnal Tecnoscienza Respati Jurnal Teknika IT (INFORMATIC TECHNIQUE) JOURNAL JOURNAL OF INFORMATION SYSTEM MANAGEMENT (JOISM) Journal of Intelligent Decision Support System (IDSS) G-Tech : Jurnal Teknologi Terapan International Journal of Advances in Data and Information Systems Jurnal Sistem Komputer dan Informatika (JSON) Journal of Innovation Information Technology and Application (JINITA) Jurnal Informa: Jurnal Penelitian dan Pengabdian Masyarakat Bulletin of Computer Science and Electrical Engineering (BCSEE) Infotek : Jurnal Informatika dan Teknologi jurnal syntax admiration Jurnal TIKOMSIN (Teknologi Informasi dan Komunikasi Sinar Nusantara) TEPIAN Infokes : Jurnal Ilmiah Rekam Medis dan Informasi Kesehatan JURNAL TEKNOLOGI TECHNOSCIENTIA Jurnal Teknik Informatika (JUTIF) Jurnal Teknimedia: Teknologi Informasi dan Multimedia JNANALOKA Journal of Electrical Engineering and Computer (JEECOM) Journal of Applied Data Sciences Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Mitra Mahajana: Jurnal Pengabdian Masyarakat Journal of Applied Computer Science and Technology (JACOST) Jurnal Pendidikan dan Teknologi Indonesia International Journal of Computer and Information System (IJCIS) Jurnal Informatika dan Teknologi Komputer ( J-ICOM) International Research on Big-data and Computer Technology (IRobot) Bulletin of Computer Science Research INFOSYS (INFORMATION SYSTEM) JOURNAL J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Saintekom : Sains, Teknologi, Komputer dan Manajemen Journal Automation Computer Information System (JACIS) Jurnal Ekonomi dan Teknik Informatika International Journal Artificial Intelligent and Informatics Jurnal Ilmiah IT CIDA : Diseminasi Teknologi Informasi Jurnal Dinamika Informatika (JDI) Jurnal Nasional Teknik Elektro dan Teknologi Informasi sudo Jurnal Teknik Informatika Jurnal Informatika Teknologi dan Sains (Jinteks) Duta.com : Jurnal Ilmiah Teknologi Informasi dan Komunikasi Prisma Sains: Jurnal Pengkajian Ilmu dan Pembelajaran Matematika dan IPA IKIP Mataram EXPLORE Journal of Comprehensive Science Techno SENTRI: Jurnal Riset Ilmiah Indonesian Journal Computer Science (ijcs) Jurnal Educative: Journal of Educational Studies Jurnal Ilmiah Sistem Informasi dan Ilmu Komputer JURNAL TEKNIK INDUSTRI STORAGE: Jurnal Ilmiah Teknik dan Ilmu Komputer Jurnal Pendidikan Indonesia (Japendi) Cerdika: Jurnal Ilmiah Indonesia International Journal of Advanced Science Computing and Engineering Innovative: Journal Of Social Science Research J-Icon : Jurnal Komputer dan Informatika Prosiding SEMNAS INOTEK (Seminar Nasional Inovasi Teknologi) SmartComp Fahma : Jurnal Informatika Komputer, Bisnis dan Manajemen Jurnal Informatika Polinema (JIP) Jurnal Informatika: Jurnal Pengembangan IT Teknomatika: Jurnal Informatika dan Komputer Proceeding of International Conference on Information Science and Technology Innovation (ICoSTEC) Tafaqquh : Jurnal Hukum Ekonomi Syariah dan Ahwal Syahsiyah Explore The Indonesian Journal of Computer Science Scientific Journal of Informatics Jurnal Teknologi KOPEMAS International Journal of Information Engineering and Science At-Tahdzib: Jurnal Studi Islam dan Muamalah semanTIK JESICA Jurnal Sistem Informasi Komputer dan Teknologi Informasi JuTISI (Jurnal Teknik Informatika dan Sistem Informasi) Jurnal Komtika (Komputasi dan Informatika)
Ema Utami
Universitas Amikom Yogyakarta
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A Systematic Literature Review of Retrieval-Augmented Generation: Methods, Applications, and Future Research Directions Muhammad Rizky Hajar; Ema Utami; Alva Hendi Muhammad
Journal of Applied Computer Science and Technology Vol. 6 No. 2 (2025): Desember 2025
Publisher : Indonesian Society of Applied Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52158/jacost.v6i2.1170

Abstract

Retrieval-Augmented Generation (RAG) represents a growing research direction in the advancement of large language models (LLMs) by incorporating external information sources into the response generation process. As LLM-based systems are increasingly deployed in information-sensitive domains such as healthcare, education, and law, the demand for responses that are not only fluent but also verifiable and context-aware has become more pronounced. This study conducts a systematic literature review (SLR) of 100 recent publications to examine methodological approaches, application domains, technical challenges, and research contributions related to RAG. The review draws on studies indexed in major academic databases, including IEEE, ACM, and Springer, and applies structured inclusion and exclusion criteria to ensure analytical rigor. The findings reveal a strong emphasis on architectural optimization, particularly in the interaction between retrieval and generation components, alongside widespread adoption in domain-specific contexts. Persistent challenges identified across the literature include limitations in retriever effectiveness, system integration complexity, and the absence of standardized evaluation benchmarks. Overall, this review provides a structured synthesis of current RAG research and highlights directions for future investigation and practical deployment..
Performance Comparison of Tree-Based Models for Heart Disease Prediction Using Feature Selection and SMOTE Santi Santi; Ema Utami
JUITA: Jurnal Informatika JUITA Vol. 14 Issue 2, July 2026
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v14i2.29098

Abstract

Heart disease remains the leading cause of mortality worldwide, highlighting the need for accurate early prediction models. This study proposes a machine learning framework for heart disease prediction using the BRFSS 2015 Heart Disease Health Indicators dataset, which contains 253,680 records and 22 attributes. The proposed approach integrates Synthetic Minority Oversampling Technique (SMOTE) for class imbalance handling, mutual information-based SelectKBest feature selection (k = 15), and three tree-based classifiers: Decision Tree, Random Forest, and XGBoost. A leakage-free preprocessing pipeline was implemented to ensure that SMOTE was applied only to the training data, and classification threshold optimization was performed to improve minority class detection. Model performance was evaluated using Accuracy, Precision, Recall, F1-score, and ROC-AUC metrics. Experimental results show that XGBoost achieved the best performance with a cross-validation ROC-AUC of 0.9815 and a test ROC-AUC of 0.8444 at an optimized threshold of 0.20. The findings demonstrate that the proposed integration of oversampling, feature selection, and threshold optimization can improve predictive performance for imbalanced cardiovascular risk data, providing a practical foundation for machine learning–based decision support in early heart disease risk screening.  
ANFIS Kuantisasi Ringan yang Terkoordinasikan di Tepi untuk Prediksi Iklim Mikro Adaptif dalam Sistem IoT yang Dibatasi Energi Eddy Nurraharjo; Ema Utami; Kusrini Kusrini; Kumara Ari Yuana
JUITA: Jurnal Informatika JUITA Vol. 14 Issue 2, July 2026
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v14i2.30160

Abstract

This study addresses the challenge of deploying collaborative adaptive intelligence on energy-constrained IoT edge nodes, a critical requirement for real-time microclimate prediction in smart agriculture. We propose a novel Edge-Coordinated Lightweight Quantized Adaptive Neuro-Fuzzy Inference System (LQ-ANFIS). The framework combines a quantized fuzzy neural model for local inference on 8-bit microcontrollers (WeMOS D1 Mini) with a lightweight MQTT-based coordination mechanism. This mechanism enables distributed nodes to achieve synchronized adaptation by periodically exchanging only scalar parameters, namely bias and learning rate, through a broker (Raspberry Pi), thereby eliminating the need for cloud infrastructure or heavy model transfers. During a seven-day experimental deployment involving three nodes, each collecting more than 10,000 temperature and humidity samples, the system demonstrated robust prediction accuracy (RMSE ≈ 1.89%, MAE ≈ 1.13%) and high energy efficiency (average power < 100 mW per node). Compared with a conventional uncoordinated ANFIS baseline, the proposed method achieved a 21% improvement in prediction accuracy and a 39% reduction in energy consumption. The results confirmed rapid inter-node bias convergence (σ < 0.05) and low coordination latency (< 50 ms), validating its real-time adaptability at the edge. These findings support scalable, cooperative intelligence for resource-constrained agricultural IoT deployments worldwide.
Studi Komparasi Metode SVM dan Naive Bayes pada Data Bencana Banjir di Indonesia Riska K Abdullah; Ema Utami
JURNAL TECNOSCIENZA Vol. 3 No. 1 (2018): JURNAL TECNOSCIENZA
Publisher : JURNAL TECNOSCIENZA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51158/zdjp6p17

Abstract

Studi Komparasi Metode SVM dan Naive Bayes Pada Data Bencana Banjir di Indonesia bertujuan untuk mendapatkan dataset clean yang berisi bencana banjir lengkap dengan atribut cuaca. Pada dataset tersebut kemudian diimplementasikan model klasifikasi SVM dan Naive Bayes. Proses ini dilakukan agar performa antara SVM dan Naive Bayes dapat terlihat dan bisa dinilai mana yang lebih baik ketika diterapkan pada data bencana banjir di Indonesia. Penelitian dibagi menjadi tiga tahap utama, tahap pertama yaitu proses ekstraksi dataset. Proses tersebut bertujuan untuk mendapatkan dataset yang clean. Proses tersebut dilaksanakan dengan penerapan teknik data mining untuk menyatukan data cuaca dan data bencana alam berdasarkan tanggal dan lokasi kejadian. Tahap kedua yaitu proses implementasi klasifikasi, dan tahap terakhir yaitu proses capturing performa dari kedua model. Pada tahap terakhir pengukuran performa dari kedua model (SVM dan Naive Bayes) didapatkan dari Perhitungan akurasi dengan memanfaatkan confusion matrix, analisa ROC, kemudian parameter perbandingan selanjutnya yaitu waktu eksekusi. Hasil dari penelitian menunjukkan persentase Nilai akurasi rata-rata dari model SVM sebesar 48,90% sedangkan nilai akurasi dari Naive Bayes sebesar 64,70%. Sementara itu untuk masing-masing runtime SVM kurang lebih sebesar 720 mili detik dan naive bayes kurang lebih 280 mili detik. Dapat disimpulkan bahwa metode Naive Bayes lebih baik performanya dibandingkan dengan metode SVM ketika kedua metode tersebut diterapkan pada dataset yang sama yaitu dataset bencana banjir di Indonesia. Begitu pun dengan runtime, Naive Bayes masih lebih unggul karena memiliki waktu yang lebih singkat dalam proses trainning dan testing dibandingkan dengan SVM. Kata kunci: svm, naïve bayes, comparative, classification
Literatur Review Bat Algorithm Terhadap Analisis Sentimen Pada Lini Masa Twitter Candra Adipradana; Ema Utami; Anggit Dwi Hartanto
JURNAL TECNOSCIENZA Vol. 5 No. 1 (2020): JURNAL TECNOSCIENZA
Publisher : JURNAL TECNOSCIENZA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51158/v4mwd237

Abstract

Algoritma metaheuristik seperti particle swarm optimization, firefly algorithm and harmony sekarang menjadi metode yang kuat untuk menyelesaikan banyak masalah optimasi yang sulit. Dalam literature review ini, kami mengusulkan suatu metode metaheuristik baru yaitu Binary Bat Algorithm atau Algoritma Kelelawar dengan Biner, hal ini didasarkan pada perilaku ekolokasi kelelawar. Kami juga berniat untuk menggabungkan keunggulan dari algoritma yang ada ke dalam algoritma kelelawar baru. Setelah perumusan terperinci dan penjelasan implementasinya, kami akan melakukannya perbandingan algoritma yang diusulkan dengan algoritma lain yang ada, termasuk genetic algorithms and particle swarm optimization. Simulasi menunjukkan bahwa algoritma yang diusulkan tampaknya jauh lebih unggul daripada algoritma lainnya, dan kedepannya studi lebih lanjut juga akan dibahas. Kata kunci: Biner, Ekolokasi, Metaheuristik, Algoritma Kelelawar
Klasifikasi Kepribadian Dengan Metode DISC Pada Twitter Menggunakan Algoritma Artificial Neural Network Idris Idris; Ema Utami; Anggit Dwi Hartanto
JURNAL TECNOSCIENZA Vol. 5 No. 1 (2020): JURNAL TECNOSCIENZA
Publisher : JURNAL TECNOSCIENZA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51158/3s308g79

Abstract

Maju mundurnya suatu perusahaan biasanya didukung oleh adanya sumber daya yang handal, terutama sumber daya manusia. Perekrutan dan penempatan pegawai pada posisi yang tepat akan membawa dampak yang signifikan bagi suatu perusahaan. Di dunia ini sifat dan karakter manusia sangat beraneka ragam bentuknya. Teori DISC mengklasifikasikan kepribadian menjadi empat tipe yaitu dominance, influence, steadiness dan compliance. Perbedaan karakter setiap tipe tentu saja akan berpengaruh pada gaya perilaku, cara menghadapi tekanan hidup dan juga cara berkomunikasi baik secara langsung maupun dengan media sosial. Melalui sosial media, seseorang dapat meluapkan perasaanya melalui postingan yang diunggahnya. Dari postingan tersebut dapat dilakukan analisis mengenai karakter kepribadian yang ia dimiliki. Penelitian ini bertujuan untuk mengetahui seberapa besar akurasi analisis profiling pada Twitter sehingga bisa menjadi acuan untuk proses perekrutan pegawai. Penelitian ini menggunakan algoritma Artificial Neural Network untuk mengklasifikasikan 275 akun Twitter kedalam teori DISC dan mendapatkan akurasi sebesar 42,91% dari 72 skenario yang dijalankan. Kata kunci: Kepribadian DISC, Media Sosial, Analisis Profiling, Sumber Daya Manusia
Analisis Sentimen Pada Kurikulum Merdeka Menggunakan Word2Vec Dan Algoritma Long Short-Term Memory Octhavia Alin; Ema Utami
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 10, No 3 (2025)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v10i3.6516

Abstract

Kurikulum Merdeka, sebagai penyempurnaan dari Kurikulum 2013, telah memicu berbagai opini publik sejak diterapkan pada Tahun Ajaran 2022/2023. Opini-opini ini, yang dibagikan melalui platform media sosial seperti Twitter dan YouTube, mencerminkan pandangan beragam dari masyarakat. Penelitian ini menganalisis sentimen terhadap Kurikulum Merdeka menggunakan algoritma LSTM yang dipadukan dengan Word2Vec. Dataset yang digunakan terdiri dari 23.577 teks dari Twitter dan YouTube, yang dikategorikan menjadi sentimen negatif, positif, dan netral. Penelitian ini membandingkan dua arsitektur Word2Vec. Skip-gram dan Continuous Bag of Words (CBOW), serta mengeksplorasi pengaruh optimizer (Adam dan RMSprop) dan learning rate terhadap kinerja model. Hasil penelitian menunjukkan bahwa arsitektur CBOW, yang dikombinasikan dengan optimizer Adam dan learning rate 0.001, memberikan hasil yang paling akurat dan stabil, dengan akurasi mencapai 97% dan tingkat loss yang rendah. Skip-Gram lebih sensitif terhadap perubahan parameter, yang menyebabkan ketidakstabilan kinerja. Penelitian ini menegaskan efektivitas kombinasi Word2Vec dan LSTM untuk analisis sentimen, dengan CBOW dan optimizer Adam sebagai konfigurasi yang paling optimal
IMPLEMENTATION OF WORD EMBEDDING IN DETECTING POLITICAL FAKE NEWS IN INDONESIA USING LONG SHORT-TERM MEMORY ALGORITHM Anggit Rianansyah; Ema Utami; Dhani Ariatmanto
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 10, No 4 (2025)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v10i4.6680

Abstract

This research investigates the use of word embedding techniques to detect political fake news in Indonesia by utilizing the Long Short-Term Memory (LSTM) algorithm. The spread of fake news, particularly in the political realm, poses significant challenges to public trust and the integrity of information. To address these challenges, we employed a dataset of political news articles and applied word embedding to convert the text into a numerical format that represents the semantic relationships between words. The LSTM algorithm, known for its ability to process and learn from sequential data, was then used to identify patterns indicative of fake news. Our model demonstrated satisfactory accuracy, with the LSTM algorithm without word embedding achieving an average accuracy of 67%, while the application of word embedding (Word2Vec, Glove, and FastText) resulted in average accuracies of 84%, 81%, and 86%, respectively. These findings confirm that combining word embedding with LSTM is effective in detecting fake news. This research contributes to ongoing efforts to combat misinformation in Indonesia by providing a robust tool to enhance the reliability of news in the digital age. Further developments, such as the integration of additional linguistic features and the expansion of the dataset, are expected to improve the model’s performance and adaptability across various contexts.
IDENTIFIKASI PENYAKIT TANAMAN JAGUNG BERDASARKAN CITRA DAUN MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK Dwi Septiyani Arwin; Ema Utami
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 10, No 4 (2025)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v10i4.9320

Abstract

Identifikasi penyakit pada daun jagung memiliki peran penting dalam mendukung produktivitas pertanian yang berkelanjutan. Penelitian ini mengusulkan model klasifikasi menggunakan Convolutional Neural Network (CNN) dengan arsitektur ResNet-50 untuk mendeteksi penyakit daun jagung berdasarkan data citra. Dataset yang digunakan terdiri dari empat kategori: Blight, Common Rust, Gray Leaf Spot, dan Healthy, yang diperoleh dari sumber data publik. Pra-pemrosesan data dilakukan melalui perubahan ukuran citra menjadi 224×224 piksel dan peningkatan kontras menggunakan teknik CLAHE. Pengembangan model dilakukan dengan pendekatan transfer learning dan dievaluasi menggunakan metrik akurasi, presisi, recall, F1-score, serta confusion matrix. Hasil eksperimen menunjukkan kinerja klasifikasi yang tinggi dengan akurasi keseluruhan sebesar 94%. Kategori Healthy memperoleh performa terbaik (presisi dan recall = 1.00), sementara kategori Gray Leaf Spot mencatat nilai recall terendah akibat kemiripan visual dengan kategori lain. Penelitian ini membuktikan bahwa model CNN berbasis ResNet-50 sangat efektif untuk klasifikasi otomatis penyakit daun jagung dan berpotensi besar untuk diterapkan dalam sistem pertanian presisi.
OPTIMIZATION OF DIABETES PREDICTION USING MACHINE LEARNING MODELS: THE ROLE OF PREPROCESSING AND TEST SIZE VARIATIONS Irfan Kurniawan; Ema Utami
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 10, No 1 (2025)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v10i1.7454

Abstract

Diabetes mellitus remains one of the fastest-growing non-communicable diseases in the world, with global prevalence pro-jected to rise from 463 million cases in 2019 to 578 million by 2030 and 700 million by 2045. Timely and accurate risk prediction is therefore essential for early clinical intervention. This study exam-ines how data preprocessing strategies and train–test split ratios jointly affect the predictive accuracy of three widely used machine learning classifiers Random Forest, Support Vector Machine (SVM), and Naïve Bayes applied to the Pima Indians Diabetes Database (PIDD). Physiologically implausible zero values in the Glucose, Blood Pressure, Skin Thickness, Insulin, and BMI attributes were treated as missing and imputed using the median owing to the pres-ence of substantial outliers. Three scaling techniques (Min-Max, Standard, and Robust Scaling) were then compared against the un-scaled dataset across three train–test split configurations (0.1, 0.2, and 0.3). The experimental results show that Random Forest con-sistently achieved the highest accuracy across every preprocessing scenario, peaking at 89.61% with Min-Max Scaling and a 0.1 test size. SVM benefited considerably from feature scaling, reaching 87.01% accuracy under the same configuration, while Naïve Bayes performed best on the untransformed data (81.82%) and slightly declined after scaling, confirming its comparative insensitivity to feature magnitude. Further analysis indicates that, because tree-splitting criteria are theoretically invariant to monotonic feature transformation, most of the accuracy gain attributed to “prepro-cessing” in Random Forest is more plausibly explained by the medi-an-imputation step than by scaling itself a distinction not previously articulated in this line of research. These findings demonstrate that the benefit of preprocessing is algorithm-dependent rather than uni-versal, and that smaller test sizes generally favor tree-based ensem-bles by providing more training instances. The study contributes empirical evidence and a practical decision framework for selecting preprocessing pipelines according to classifier characteristics, ra-ther than assuming a one-size-fits-all approach in diabetes predic-tion research.
Co-Authors , Anggit Dwi Hartanto A.A. 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