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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika METIK JURNAL Jurnal Mantik Jusikom: Jurnal Sistem Informasi Ilmu Komputer Community Engagement and Emergence Journal (CEEJ) Jurnal Teknik Informatika C.I.T. Medicom Jurnal Sistem Komputer dan Informatika (JSON) Jurnal Pendidikan dan Teknologi Indonesia Jurnal Pengabdian kepada Masyarakat Politeknik Negeri Batam Data Sciences Indonesia (DSI) Jurnal Informatika: Jurnal Pengembangan IT
Yennimar, Yennimar
Unknown Affiliation
Humanities 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 Languange, Linguistic, Communication & Media Mechanical Engineering Physics

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Deteksi Kelainan Jantung Berdasarkan Sinyal EKG Menggunakan Deep Neural Network Robert, Michael; yennimar, Yennimar; wyjaya, Andy; Ebert, Steven; Ali Ramadhan, Mhd
Jurnal Sistem Komputer dan Informatika (JSON) Vol 6, No 4 (2025): Juni 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v6i4.8662

Abstract

Penyakit jantung merupakan salah satu penyebab utama kematian di dunia, termasuk di Indonesia. Deteksi dini kelainan jantung melalui sinyal elektrokardiogram (EKG) sangat penting, namun interpretasi manual oleh tenaga medis sering kali memerlukan keahlian khusus dan rentan terhadap kesalahan. Penelitian ini bertujuan untuk mengembangkan sistem deteksi otomatis kelainan jantung menggunakan metode Deep Neural Network (DNN) berdasarkan sinyal EKG. Dataset yang digunakan berasal dari PTB Diagnostic ECG Database (PTBDB) yang diperoleh dari Kaggle, dengan dua kategori data: normal dan abnormal. Data diproses melalui tahap balancing, normalisasi, dan pembagian menjadi data latih dan uji. Model DNN dilatih menggunakan data terstruktur berdurasi pendek dengan 187 fitur, dan dievaluasi menggunakan metrik akurasi, precision, recall, f1-score, ROC, serta Precision-Recall Curve. Hasil pelatihan menunjukkan bahwa model mampu mencapai akurasi validasi sebesar 95% dan nilai AUC sebesar 0,98, yang mengindikasikan kemampuan klasifikasi yang sangat baik. Dengan performa tersebut, model ini memiliki potensi besar untuk diterapkan dalam sistem pendukung diagnosis medis secara real-time, terutama untuk membantu deteksi dini gangguan jantung secara efisien dan akurat
Implementasi Naïve Bayes untuk Rekomendasi Pembelian Produk pada Aplikasi E-commerce Situngkir, Boy Betrand; Limbong, Endson Danielgar; Pandiangan, Very Andreas; siagian, Rivaldo calvin; Yennimar, Yennimar
Jurnal Informatika: Jurnal Pengembangan IT Vol 10, No 3 (2025)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v10i3.8880

Abstract

Electronic commerce (e-commerce) is a platform that influences buying and selling habits in Indonesia, with data from the Central Statistics Agency 2023 showing 31,753 e-commerce businesses using consumer review data as a determinant of product and service quality. This research aims to develop a sentiment-based product recommendation system using the Naïve Bayes algorithm. The research methodology includes collecting 1,287 data samples obtained from customer reviews using Web Scraper technology on the official MSI Official Store e-commerce platforms in the Tokopedia, Shopee, and Blibli applications. The results of data preprocessing yielded 921 clean data, and the Naïve Bayes Algorithm was applied as a classification model and system implementation in a website application. The data was then divided into 80% for training and 20% for testing. Model evaluation showed an accuracy of 82% for training data and 71% for testing data. These results indicate the effectiveness of the Naïve Bayes algorithm in forming a sentiment-based product recommendation system. This recommendation system helps users make more informed purchasing decisions based on consumer sentiment analysis. This research contributes to the development of intelligent recommendation systems that can improve user decision-making in the digital market
Implementasi Data Mining Untuk Memprediksi Konsumsi Bahan Bakar Menggunakan Metode Regualized Linear Regression Kamti, Elvies; Felim, Raynaldi; Chandra, Irvin; Yennimar, Yennimar
Community Engagement and Emergence Journal (CEEJ) Vol. 6 No. 5 (2025): Community Engagement & Emergence Journal (CEEJ)
Publisher : Yayasan Riset dan Pengembangan Intelektual

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37385/ceej.v6i5.9174

Abstract

Pesatnya perkembangan industri otomotif di era globalisasi telah meningkatkan persaingan dan mendorong kebutuhan akan efisiensi bahan bakar kendaraan. Prediksi konsumsi bahan bakar menjadi krusial untuk menekan biaya operasional, mengurangi emisi karbon, serta mendukung kebijakan lingkungan yang berkelanjutan. Penelitian ini bertujuan untuk membangun model prediksi konsumsi bahan bakar kendaraan menggunakan metode Regularized Linear Regression dengan memanfaatkan data kendaraan seperti Vehicle Class, Engine Size, Cylinders, Transmission, dan CO Emissions.Model dibangun berdasarkan 639 data dari 36 merek mobil, termasuk Suzuki, Honda, Audi, dan Toyota. Hasil analisis menunjukkan bahwa metode ini mampu memberikan tingkat akurasi sebesar 79% berdasarkan nilai R-squared dan memiliki Mean Squared Error (MSE) senilai 2.01, yang menunjukkan performa prediksi yang cukup baik. Pendekatan ini dibandingkan dengan beberapa penelitian sebelumnya dan menunjukkan peningkatan dalam penyajian akurasi serta pengukuran kesalahan prediksi. Penelitian ini memberikan kontribusi yang cukup besar dalam perkembangan sistem prediksi konsumsi bahan bakar yang lebih akurat, yang dapat digunakan oleh produsen kendaraan, perusahaan transportasi, dan pembuat kebijakan untuk meningkatkan efisiensi dan keberlanjutan sektor transportasi.
Comparison of C4.5 & Random Forest Based on AdaBoost For Determining Loan Eligibility Customer Funds Lenny, Lenny; Violyn, Violyn; Ridwan, Achmad; Yennimar, Yennimar
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 1 (2025): Research Article, January 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i1.14499

Abstract

This research discusses the comparison between two data mining algorithms, namely Decision Tree C4.5 and Random Forest based on AdaBoost, in determining the creditworthiness of customer funds. The main objective of this research is to evaluate and compare the performance of the two algorithms in predicting loan eligibility based on customer data. Algorithm performance is measured using accuracy, precision, recall, and misclassification error metrics. The research results show that the AdaBoost-based Random Forest is superior with an accuracy of 78.86%, recall of 98.75%, and the lowest misclassification error of 21.14%. Meanwhile, Decision Tree C4.5 provides lower performance than AdaBoost-based Random Forest. This research recommends further exploration of other algorithms, such as Support Vector Machine (SVM) and Neural Networks, to obtain more optimal results in determining customer loan eligibility.
Pelatihan Internet Of Things (IoT) Untuk Meningkatkan Kompetensi Digital Siswa Di Smk Negeri Jorlang Hataran Perangin Angin, Despaleri; Gultom, Togar Timoteus; Sitanggang, Delima; Yennimar, Yennimar; Prabowo, Agung; Siregar, Saut Dohot; Ridwan, Achmad; Ginting, Riski Titian; HS, Christnatalis; Manday, Dhanny Rukmana
Jurnal Pengabdian kepada Masyarakat Politeknik Negeri Batam Vol. 7 No. 1 (2025): Jurnal Pengabdian kepada Masyarakat Politeknik Negeri Batam
Publisher : Politeknik Negeri Batam

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

Abstract

The purpose of this community service activity is to enhance digital competency skills at SMK Negeri I Jorlang Hataran. The method used in the implementation of this activity is training through the delivery of materials, practical training on the assembly and programming of IoT devices, and a question-and-answer session. The participants of this activity consist of 37 students from the 11th grade RPL (Software Engineering) major. The instruments used in this activity include participant feedback and activity documentation. The results of the implementation show that the participants' responses to the basic computer training were overall in the good category. The percentage of student responses reached 98.20%, which falls into the very good category.
Deteksi Kelainan Jantung Berdasarkan Sinyal EKG Menggunakan Deep Neural Network Robert, Michael; yennimar, Yennimar; wyjaya, Andy; Ebert, Steven; Ali Ramadhan, Mhd
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 6 No. 4 (2025): Juni 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v6i4.8662

Abstract

Penyakit jantung merupakan salah satu penyebab utama kematian di dunia, termasuk di Indonesia. Deteksi dini kelainan jantung melalui sinyal elektrokardiogram (EKG) sangat penting, namun interpretasi manual oleh tenaga medis sering kali memerlukan keahlian khusus dan rentan terhadap kesalahan. Penelitian ini bertujuan untuk mengembangkan sistem deteksi otomatis kelainan jantung menggunakan metode Deep Neural Network (DNN) berdasarkan sinyal EKG. Dataset yang digunakan berasal dari PTB Diagnostic ECG Database (PTBDB) yang diperoleh dari Kaggle, dengan dua kategori data: normal dan abnormal. Data diproses melalui tahap balancing, normalisasi, dan pembagian menjadi data latih dan uji. Model DNN dilatih menggunakan data terstruktur berdurasi pendek dengan 187 fitur, dan dievaluasi menggunakan metrik akurasi, precision, recall, f1-score, ROC, serta Precision-Recall Curve. Hasil pelatihan menunjukkan bahwa model mampu mencapai akurasi validasi sebesar 95% dan nilai AUC sebesar 0,98, yang mengindikasikan kemampuan klasifikasi yang sangat baik. Dengan performa tersebut, model ini memiliki potensi besar untuk diterapkan dalam sistem pendukung diagnosis medis secara real-time, terutama untuk membantu deteksi dini gangguan jantung secara efisien dan akurat
Comparison of data mining algorithms (random forest, C4.5, catboost) based on adaptive boosting in predicting diabetes mellitus Yennimar, Yennimar; Leonardi, William; Weide, Harris; Cantona, Devin; Hutagalung, Gani Mores
Jurnal Teknik Informatika C.I.T Medicom Vol 16 No 1 (2024): March: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/cit.Vol16.2024.730.pp1-12

Abstract

This research aims to evaluate the performance of three algorithms data mining, namely C4.5, Random Forest, and Catboost Classifier, which are strengthened by Adaptive Boosting in predicting diabetes mellitus in humans. Through analysis, it was found that the C4.5 algorithm is based on Adaptive Boosting obtained an average accuracy of 73.74%, precision of 61.39%, and recall amounting to 69.00%. Random Forest algorithm based on Adaptive Boosting shows an average accuracy of 73.52%, precision of 65.79%, and recall amounting to 65.06%. Meanwhile, the Catboost Classifier algorithm is Adaptive based Boosting has an average accuracy of 73.67%, precision of 61.19%, and recall was 69.18%. Thus, although all three algorithms shows similar performance, the C4.5 algorithm based on Adaptive Boosting stands out with better performance in terms of accuracy, precision and recall. The implication of this research is that the use of the C4.5 algorithm is based Adaptive Boosting can be a more effective approach to support early detection of diabetes mellitus in humans
Co-Authors Achmad Ridwan Achmad Ridwan, Achmad Agung Prabowo Ali Ramadhan, Mhd Aprillia S, Debora Bayu Angga Wijaya Cantona, Devin Chandra, Irvin Delima Sitanggang, Delima Dharsinni, N P Ebert, Steven Fawwaz, Insidini Felim, Raynaldi Ginting, Riski Titian Hia, Michael Justin Gesitera HS, Christnatalis Hutagalung, Gani Mores Hutagalung, Samuel Valentino Jauhary, Wilson Kamti, Elvies Kenedy, Sun Lenny, Lenny Leonardi, William Limbong, Endson Danielgar Lubis , Nadya Khairunisa Manday, Dhanny Rukmana Mawaddah Harahap, Mawaddah Medina, Nabila Pandiangan, Very Andreas Perangin Angin, Despaleri Pratama, Ahlul Yoga Rangga Aditya , Muhammad Rasid, Alvin Rizal, Reyhan Achmad Robert, Michael Rozi, Fachrul Rumapea, Erikson Roni Sanjaya, Gauri Ananda Sembiring, Terkelin siagian, Rivaldo calvin Simarmata, Allwin M Siregar, Saut Dohot Situngkir, Boy Betrand Togar Timoteus Gultom Violyn, Violyn Weide, Harris wyjaya, Andy Yaphentus, Albert Julius Yusna, Pradana Zega, Ide Kristiani