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All Journal Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Speed - Sentra Penelitian Engineering dan Edukasi Jurnal Teknologi Informasi dan Ilmu Komputer JIKO (Jurnal Informatika dan Komputer) Jurnal TAM (Technology Acceptance Model) Abdimas: Jurnal Pengabdian Masyarakat Universitas Merdeka Malang International Journal of Engineering, Technology and Natural Sciences (IJETS) Jurnal Berdaya Mandiri Indonesian Journal of Data and Science SPEED - Sentra Penelitian Engineering dan Edukasi Jurnal Saintekom : Sains, Teknologi, Komputer dan Manajemen Fahma : Jurnal Informatika Komputer, Bisnis dan Manajemen International Journal of Artificial Intelligence in Medical Issues Jurnal TAM (Technology Acceptance Model)
Edi Faizal
Prodi Rekayasa Perangkat Lunak Aplikasi, FTI, Universitas Teknologi Digital Indonesia
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An Optimization Strategy for Reducing CO₂ in Livestock Farming with IoT Integration and Decision Support System Approach Using Linear Programming Shimbun, Annisa Fikria; Alfian, Muhammad Arif; Jati, Agam Saka; Faizal, Edi
Indonesian Journal of Data and Science Vol. 6 No. 1 (2025): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v6i1.204

Abstract

Introduction: Livestock waste mismanagement contributes significantly to CO₂ emissions, adversely affecting animal health and environmental sustainability. This study aims to develop an optimization strategy for reducing CO₂ levels in livestock environments through the integration of Internet of Things (IoT) technology and a Decision Support System (DSS) using Linear Programming. Methods: IoT sensors were deployed to monitor environmental parameters such as CO₂ levels, temperature, and humidity in real time. A Linear Programming (LP) model was formulated to determine the optimal frequency of two CO₂-reducing actions: spraying Effective Microorganisms (EM4) and performing waste dredging. The objective was to maximize CO₂ reduction under cost and time constraints. The model iteratively updated its parameters based on sensor data feedback, ensuring dynamic and adaptive optimization. Results: Simulation results indicated that the LP model successfully identified optimal actions within predefined constraints. The optimal strategy was spraying EM4 eight times over eight days, achieving a CO₂ reduction of 800 ppm with a total cost of Rp 400,000—within the Rp 500,000 budget limit and 8-hour duration constraint. Validation through simulation confirmed the model’s accuracy, with prediction deviations consistently falling within an acceptable threshold (±20 ppm). Conclusions: The integration of IoT with an LP-based DSS offers a practical and efficient solution for CO₂ reduction in livestock farming. This system enhances decision-making for environmental management, demonstrating potential for scalable application in sustainable agriculture. Future work should incorporate more environmental variables and broader validation to improve model generalizability and precision.
Implementation of Support Vector Machine Algorithm for Classification of Study Period and Graduation Predicate of Students Sumiyatun; Cahyadi, Yagus; Faizal, Edi
Indonesian Journal of Data and Science Vol. 6 No. 1 (2025): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v6i1.214

Abstract

Introduction: Accurately predicting the duration of study and graduation predicates in higher education is essential for improving academic outcomes and decision-making. This study aims to classify students’ study period and graduation predicates in the Information Systems program at UTDI using the Support Vector Machine (SVM) algorithm. Methods: A dataset of 500 student records containing academic and demographic variables—including GPA, age, semesters, and graduation predicates—was processed through data cleaning, normalization, and feature selection. Study duration was categorized into three classes: short (≤4 years), medium (4–6 years), and long (>6 years). An SVM with a linear kernel was applied, and the model was evaluated using accuracy, precision, recall, and F1-score. Results: The SVM model achieved perfect classification for study duration, with 100% accuracy, precision, recall, and F1-score across all categories. For graduation predicate classification, the model attained 95.18% accuracy. While it performed well overall, it faced some difficulty distinguishing between "Cum Laude" and "Very Satisfactory" due to overlapping GPA ranges. The analysis identified GPA as the most influential feature in both classification tasks, while age and the number of semesters played supporting roles. Conclusions: The SVM model demonstrates strong capability in classifying study duration and graduation predicates, offering valuable insights for academic management. Although performance was high, especially for study period prediction, further refinement is suggested to enhance classification in overlapping categories. Future work may benefit from larger, more balanced datasets and exploration of advanced models to increase prediction reliability.
FRAMEWORK PENGEMBANGAN CITY BRANDING KABUPATEN BANTUL MENGGUNAKAN PENDEKATAN SMART TOURISM Sri Redjeki; Edi Faizal; Edi Iskandar; Dedi Rosadi; Khabib Mustofa
Jurnal TAM (Technology Acceptance Model) Vol 9, No 2 (2018): Jurnal TAM (Technology Acceptance Model)
Publisher : LPPM STMIK Pringsewu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56327/jurnaltam.v9i2.656

Abstract

Perkembangan sektor pariwisata secara terarah dan berkesinambungan dapat dijadikan sebagai salah satu solusi meningkatkan pertumbuhan ekonomi suatu daerah. Dengan berkembangnya sektor pariwisata, dapat meningkatkan citra sebuah daerah yang sekaligus dapat meningkatkan pendapatan asli daerah. Pengelolaan pariwisata yang baik oleh sebuah kota dapat menjadi sebuah branding yang dapat meningkatkan kunjungan wisatawan. Pencapaian ini dapat terpenuhi dengan cepat melalui penggunaan teknologi informasi dalam pengelolaan wisata. Kabupaten Bantul dikenal sebagai salah satu Kabupaten di Yogyakarta karena obyek wisata yang memikat para wisatawan dan saat ini sedang mengembangkan konsep smart city.Penelitian ini bertujuan untuk mengembangkan model dalam implementasi salah satu komponen smart city yaitu smart branding dengan menggunakan pendekatan smart tourism di Kabupaten Bantul. Model ini dapat dikembangkan karena wilayah Kabupaten Bantul merupakan salah satu tujuan wisata utama di Yogyakarta dengan berbagai jenis wisata yang ada. Total obyek wisata di Kabupaten Bantul sebanyak 113 obyek wisata. Smart tourism yang dimodelkan pada penelitian ini adalah sistem wisata integratif yang meliputi sistem berbasis mobile, sistem pemetaan wisata, sistem desa wisata dan sistem pengolahan data. Sistem ini dapat digunakan oleh pelaku dunia wisata, pengunjung wisata dan pihak pengambil keputusan di Kabupaten Bantul Dengan model pendekatan smart tourism maka Kabupaten Bantul dapat melakukan percepatan pengembangan smart city melalui salah satu komponen yaitu pengembangan city branding.
Case Based Reasoning Diagnosis Penyakit Cardiovascular Dengan Metode Simple Matching Coefficient Similarity Faizal, Edi
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 1 No 2: Oktober 2014
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (731.304 KB) | DOI: 10.25126/jtiik.201412116

Abstract

Abstrak Penyakit kardiovaskuler atau cardiovascular disease (CVD) menurut definisi WHO adalah istilah bagi serangkaian gangguan jantung dan pembuluh darah.  Data badan kesehatan dunia WHO (2012) menunjukan bahwa CVD adalah faktor penyebab kematian nomor satu didunia dan berdasarkan data Riset Kesehatan Dasar (RISKESDAS) Kementerian Kesehatan Republik Indonesia tahun 2007 menunjukkan, penyakit yang termasuk kelompok CVD menempati urutan teratas penyebab kematian di Indonesia. Ditinjau dari sisi ketersediaan tenaga ahli dibidang cardiovascular, saat ini Indonesia hanya memiliki  sekitar  500 dokter spesialis penyakit jantung dan pembuluh darah. Artinya dengan jumlah penduduk Indonesia yang mencapai 240 juta, rasio dokter spesialis jantung dan pembuluh darah adalah 1:480.000 penduduk. Jumlah ini masih sangat kurang dibandingkan dengan kebutuhan penduduk di Indonesia. Rasio yang diharapkan adalah 1:250.000 penduduk. Penelitian ini menerapkan case-based reasoning untuk membangun sebuah sistem yang memiliki kemampuan untuk mendiagnosa penyakit cardiovascular berdasarkan kemiripan (similaritas) pada kasus-kasus terdahulu mengunakan metode simple matching coefficient similarity. Hasil pengujian menunjukan bahwa sistem yang dibangun memiliki nilai sensitifitas 97,06%, spesifisitas 64,29%, PPV 86,84%, NPV 90,00%, akurasi 87,50% dengan tingkat kesalahan (error rate) sebesar 12,50%. Kata kunci: cardiovascular, case based reasoning, simple matching coefficient Abstract Cardiovascular disease (CVD) according to the WHO definition is the term for a series of heart and blood vessel disorders . World Health Organization (2012) showed that CVD is the factor number one cause of death in the world and based on data from the Health Research (RISKESDAS) Ministry of Health of the Republic of Indonesia in 2007 showed, diseases including CVD group occupy the top cause of death in Indonesia. In terms of the availability of experts in the field of cardiovascular, Indonesia currently only have around 500 specialist heart and blood vessel disease (SpJP). This means that the number of Indonesian population reached 240 million, the ratio of specialist is 1:480.000 population . This amount is very less compared to the needs of the population in Indonesia . Expected ratio is 1:250,000 population. This study will apply a case -based reasoning to build a system that has the ability to diagnose cardiovascular disease based on the similarity in the previous cases using  method  of  simple  matching coefficient . Test results the system showed  of sensitivity value 97,06%, specificity of 64,29%, PPV 86,84%, NPV 90,00% and accuracy of 87,50% with an error rate of 12,50%. Keywords: cardiovascular, case-based reasoning, simple matching coefficient
Comparative Study of Machine Learning Methods for Disease Classification Based on Natural Language Symptom Descriptions Jullev Atmadji, Ery Setiyawan; Wibowo, Adityo Permana; Faizal, Edi
International Journal of Artificial Intelligence in Medical Issues Vol. 3 No. 2 (2025): International Journal of Artificial Intelligence in Medical Issues
Publisher : Yocto Brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijaimi.v3i2.361

Abstract

The growing demand for remote healthcare solutions has increased the importance of efficient disease diagnosis based on textual symptom descriptions. This study explores the application of machine learning models Multinomial Naive Bayes, Random Forest, and Support Vector Machine (SVM) to classify 24 different diseases from natural language symptom inputs. Utilizing a dataset of 1,200 balanced samples and TF-IDF for feature extraction, we trained and evaluated the models using both accuracy and cross-validation metrics. Among the models, SVM achieved the highest test accuracy of 97.5% and demonstrated consistent performance across all disease categories. These findings underscore the potential of classical machine learning approaches in enhancing digital diagnostic tools, particularly for early screening in telemedicine applications. Future work could extend this study by integrating deep learning architectures and multilingual capabilities to accommodate broader and more diverse healthcare scenarios.
A Context-Aware Itinerary Recommendation Model Based on CBR with Auto-Revise and Multi-Clustered Data Modeling Faizal, Edi; Hartati, Sri; Musdholifah, Aina
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 14, No 1: March 2026 (ACCEPTED PAPERS)
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v14i1.7000

Abstract

This study proposes an itinerary recommendation model based on Case-Based Reasoning (CBR), enhanced with an auto-revise mechanism and multi-cluster modeling using the DBSCAN algorithm. The model is developed from four primary data sources: historical travel cases, visit statistics, social media reviews, and contextual data. The auto-revise mechanism is activated when case similarity falls below 0.95, allowing solution adjustments based on six feature subsets: spatial, categorical, attraction, destination type, popularity, and visitor segmentation. Evaluation was conducted through 5-fold cross-validation and new-case testing, yielding F1-scores of 92.60% and 90.29%, respectively, while ranking performance remained consistently high across both evaluation scenarios. The model also demonstrated improvements in recommendation quality metrics, including novelty, diversity, and serendipity, alongside a reduction in average response latency from 25.53 ms to 20.09 ms. These results indicate that the proposed integrative CBR auto-revise approach, supported by contextual data and multi-cluster structuring, provides an adaptive and efficient itinerary recommendation framework suitable for real-time decision-support scenarios.
Co-Authors Adityo Permana Wibowo Aina Musdholifah Alfian, Muhammad Arif Andhina Ika Sunardi Andhina Ika Sunardi Christian Dwi Suhendra, Christian Dwi Dedi Rosadi Edi Iskandar, Edi Effan Najwaini Gusti Ahmad Syaripudin Hartati, Sri Hera Wasiati I Putu Adi Pratama Iskandar, Edi Jati, Agam Saka Jullev Atmadji, Ery Setiyawan Khabib Mustofa Kurniyati, Nany Noor Maria, Eny Pulut Suryati Purnamasar, Dwi Amalia Rezania Agramanisti Azdy, Rezania Agramanisti Shimbun, Annisa Fikria Siti Khomsah, Siti Sri Redjeki Sri Setyowati Sudarmanto Sumiyatun Sumiyatun Syam, Rahmat Fuadi Thomas Edyson Tarigan Totok Suprawoto Totok Suprawoto Yagus Cahyadi Yasinta Marto Enga