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All Journal Bulletin of Electrical Engineering and Informatics Nuansa Informatika Jurnal Informatika dan Teknik Elektro Terapan Sistemasi: Jurnal Sistem Informasi JOIV : International Journal on Informatics Visualization Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal Ilmiah Universitas Batanghari Jambi JURNAL MEDIA INFORMATIKA BUDIDARMA CogITo Smart Journal Jurnal Informatika Universitas Pamulang JITTER (Jurnal Ilmiah Teknologi Informasi Terapan) Jurnal Sisfokom (Sistem Informasi dan Komputer) ILKOM Jurnal Ilmiah JurTI (JURNAL TEKNOLOGI INFORMASI) Jurnal Teknologi Terpadu EDUMATIC: Jurnal Pendidikan Informatika Building of Informatics, Technology and Science Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Technologia: Jurnal Ilmiah Aisyah Journal of Informatics and Electrical Engineering Journal of Information Systems and Informatics Indonesian Journal of Business Intelligence (IJUBI) bit-Tech Aviation Electronics, Information Technology, Telecommunications, Electricals, and Controls (AVITEC) Respati Jurnal Abdi Insani JTIULM (Jurnal Teknologi Informasi Universitas Lambung Mangkurat) Journal of Computer System and Informatics (JoSYC) Jurnal Graha Pengabdian Infotek : Jurnal Informatika dan Teknologi jurnal syntax admiration TEPIAN Jurnal Teknologi Informatika dan Komputer Jurnal Teknik Informatika (JUTIF) Jurnal Teknimedia: Teknologi Informasi dan Multimedia JNANALOKA SENADA : Semangat Nasional Dalam MengabdI Journal of Electrical Engineering and Computer (JEECOM) Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Jurnal Informatika dan Teknologi Komputer ( J-ICOM) Jurnal Sisfotek Global Jurnal Informatika Teknologi dan Sains (Jinteks) Malcom: Indonesian Journal of Machine Learning and Computer Science Cerdika: Jurnal Ilmiah Indonesia Bulletin of Network Engineer and Informatics (BUFNETS) SENADA : Semangat Nasional Dalam Mengabdi TECHNOVATAR Intechno Journal : Information Technology Journal The Indonesian Journal of Computer Science SITEKNIK: Sistem Informasi, Teknik dan Teknologi Terapan Jurnal Teknik AMATA Jurnal TAM (Technology Acceptance Model)
Kusnawi Kusnawi
Universitas Amikom Yogyakarta
Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Medicine & Pharmacology Public Health Social Sciences Transportation Other

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KLASIFIKASI RANDOM FOREST TERHADAP DIAGNOSA PENYAKIT KANKER PAYUDARA BERDASARKAN STATUS KEGANASAN Yusrinnatul Jinana triadin; Kusrini Kusrini; Kusnawi Kusnawi
TEKNIMEDIA: Teknologi Informasi dan Multimedia Vol. 6 No. 1 (2025): Juni 2025
Publisher : Badan Penelitian dan Pengabdian Masyarakat (BP2M) STMIK Syaikh Zainuddin NW Anjani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46764/teknimedia.v6i1.259

Abstract

Breast cancer is one of the diseases with the highest mortality rate in the world. There are two types of breast cancer, namely malignant and benign. Identification of the type allows for prevention and appropriate treatment before it spreads to other organs. Therefore, a large amount of breast cancer data classification analysis is needed. Data mining techniques, such as random forest, can be used because they are able to provide accurate predictions with a low error rate. The results of this study indicate that *Random Forest is an effective and accurate method for breast cancer classification with an accuracy of 95% and an AUV-ROC value of 0.99 and a recall of 97% which shows the model's ability to distinguish the two types of breast cancer very well so that it can reduce the risk. The use of the 5-Fold Cross-Validation technique) ensures that the results obtained are stable and do not depend on certain data divisions, thereby increasing the generalization of the model. Experiments on various parameters (n_estimators, max_depth, training data size) show that the best configuration is n_estimators = 100 and max_depth = 10, which provides the optimal balance between accuracy and model complexity. This model can be applied in a **Medical Decision Support System* to assist doctors in *early detection of breast cancer*, thereby increasing the speed and accuracy of diagnosis.
PENERAPAN ALGORITMA MONTE CARLO UNTUK MEMPREDIKSI IPS DAN IPK BERDASARKAN KARAKTERISTIK MAHASISWA PERGURUAN TINGGI X DI KOTA CIREBON Malik, Husni Hidayat; Muhammad, Alva Hendi; Kusnawi, Kusnawi
TECHNOVATAR Jurnal Teknologi, Industri, dan Informasi Vol 2 No 4 (2024): OKTOBER
Publisher : Awatara Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61434/technovatar.v2i4.225

Abstract

Penelitian ini bertujuan untuk memprediksi Indeks Prestasi Semester (IPS) dan Indeks Prestasi Kumulatif (IPK) mahasiswa berdasarkan beberapa variabel karakteristik menggunakan algoritma Markov Chain Monte Carlo (MCMC). Variabel yang digunakan dalam penelitian ini meliputi program studi, golongan darah, pekerjaan ayah, pekerjaan ibu, dan jalur masuk. Prediksi nilai IPS dan IPK sangat penting untuk mengevaluasi kinerja akademik mahasiswa dan memberikan wawasan bagi kebijakan pendidikan di perguruan tinggi. Metode penelitian ini melibatkan penggunaan algoritma MCMC untuk memodelkan hubungan antara variabel karakteristik dengan IPS dan IPK. Data yang digunakan terdiri dari 250 mahasiswa, yang kemudian dibagi menjadi data pelatihan dan pengujian dengan rasio 80:20. Metrik evaluasi seperti Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), dan R-squared (R²) digunakan untuk mengevaluasi akurasi model prediksi. Hasil penelitian menunjukkan bahwa model MCMC mampu memprediksi IPS dan IPK dengan akurasi yang baik, ditunjukkan oleh nilai MAE sebesar 0.12 untuk IPS dan 0.11 untuk IPK, serta R² sebesar 0.78 untuk IPS dan 0.80 untuk IPK. Variabel program studi dan jalur masuk muncul sebagai faktor yang paling signifikan dalam mempengaruhi nilai akademik mahasiswa, sementara golongan darah memiliki pengaruh yang lebih rendah. Pekerjaan ayah dan pekerjaan ibu juga memberikan kontribusi moderat terhadap prediksi hasil akademik. Kesimpulannya, algoritma MCMC efektif digunakan untuk memprediksi IPS dan IPK berdasarkan karakteristik mahasiswa, memberikan wawasan bagi institusi pendidikan dalam mengambil keputusan terkait pembinaan dan pengelolaan akademik.
Analisa Prediksi Turnover Karyawan menggunakan Machine Learning Arief Maehendrayuga; Arief Setyanto; Kusnawi
bit-Tech Vol. 7 No. 2 (2024): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v7i2.1999

Abstract

Penelitian ini membahas penerapan machine learning untuk memprediksi turnover karyawan, yang merupakan tantangan utama dalam manajemen Sumber Daya Manusia (SDM). Turnover karyawan sering kali disebabkan oleh berbagai faktor, termasuk ketidakseimbangan kehidupan kerja, ketidakpuasan kerja, dan minimnya peluang pengembangan karier. Dalam penelitian ini, digunakan dataset IBM HR Analytics untuk menganalisis faktor-faktor yang memengaruhi turnover karyawan. Algoritma yang diterapkan meliputi Support Vector Machine (SVM) dan Random Forest. Proses penelitian dimulai dengan pengumpulan data, eksplorasi awal, praproses data, seleksi fitur, dan penyeimbangan data menggunakan teknik Synthetic Minority Over-sampling Technique (SMOTE). Evaluasi kinerja model dilakukan menggunakan confusion matrix untuk mengukur akurasi, presisi, recall, dan f1-score. Hasil analisis menunjukkan bahwa algoritma Random Forest memberikan kinerja yang lebih baik dibandingkan SVM. Random Forest mencapai akurasi 97,72%, sedangkan SVM memperoleh akurasi 92,51%. Setelah menerapkan SMOTE, akurasi meningkat menjadi 97% untuk Random Forest dan 93% untuk SVM. Selain akurasi, Random Forest juga unggul dalam metrik presisi, recall, dan f1-score, membuktikan keandalannya dalam memprediksi turnover karyawan. Temuan ini menegaskan bahwa pendekatan machine learning dapat digunakan untuk memahami pola turnover secara lebih mendalam. Dengan prediksi yang lebih akurat, perusahaan dapat merancang strategi retensi karyawan yang lebih efektif dan berbasis data, menciptakan lingkungan kerja yang mendukung produktivitas serta meningkatkan stabilitas tenaga kerja secara keseluruhan.
Komparasi Metode KNN dan Naive Bayes Terhadap Analisis Sentimen Pengguna Aplikasi Shopee Alfaris, Salman; Kusnawi
The Indonesian Journal of Computer Science Vol. 12 No. 5 (2023): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v12i5.3304

Abstract

Penelitian ini membandingkan keakuratan dan efektivitas KNN dan Naïve Bayes dalam menganalisis sentimen ulasan aplikasi Shopee di Google Playstore. Dalam penelitian ini, penulis mengumpulkan 2000 data terbaru dari ulasan aplikasi Shopee di Google Playstore dengan teknik web scraping. Data tersebut kemudian dibersihkan dan diberi label, menghasilkan 707 ulasan positif dan 1293 ulasan negatif. Proses preprocessing dilakukan, termasuk case folding, tokenisasi, filtering, dan stemming. Setelah tahap pengolahan data, penulis menerapkan algoritma K-Nearest Neighbor (KNN) dengan tingkat akurasi 70%. Data uji terdiri dari 400 data (20% dari total data), dengan 268 ulasan negatif dan 132 ulasan positif. Sementara itu, metode Naïve Bayes Classifier mencapai tingkat akurasi 71%. Data uji yang digunakan sama dengan KNN. Hasil penelitian menunjukkan bahwa Naïve Bayes Classifier memiliki tingkat akurasi yang lebih tinggi dibandingkan KNN. Penelitian ini diharapkan memberikan pemahaman tentang penggunaan KNN dan Naïve Bayes dalam menganalisis sentimen pengguna aplikasi Shopee di Google Playstore.
Komparasi Algoritma Supervised Learning dan Feature Selection pada Klasifikasi Penyakit Gagal Jantung Kusnawi, Kusnawi; Khrisna Irham Fadhil Pratama
The Indonesian Journal of Computer Science Vol. 12 No. 6 (2023): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v12i6.3487

Abstract

Penyakit gagal jantung merupakan penyakit yang mematikan yang ada di dunia, gagal jantung terjadi karena kondisi atau adanya kelainan otot-otot pada jantung. Pada tahun 2021 data yang ada pada WHO kematian dikarenakan penyakit jantung mencapai 17,8 juta jiwa. Salah satu cara yang dapat dilakukan yaitu dengan klasifikasi dengan menggunakan dataset public kaggle. Penelitian ini bertujuan mengkomparasi algoritma supervised learning dan metode feature selection yang terbaik, guna memperoleh hasil analisis data dengan akurasi yang baik dalam klasifikasi. Penerapan algoritma SVM, KNN, Naïve Bayes tanpa menggunakan feature selection algoritma SVM unggul menghasilkan accuracy 88.41%. Penerapan forward selection pada algoritma SVM, KNN, Naïve Bayes, algoritma SVM unggul dengan nilai accuracy 89.86%. Penerapan pearson corellation pada algoritma SVM, KNN, Naïve Bayes, algoritma KNN unggul menghasilkan accuracy 90.58%. Penerapan feature selection baik forward selection dan pearson corellation mampu meningkatkan performa akurasi, akan tetapi penerapa pearson corellation pada penelitian ini lebih baik dalam meningkatkan akurasi.
Ekspresi Emosi Berdasarkan Suara Menggunakan Algortima Multi Layer Perceptron dan Support Vector Machine Qurniaty, Charlen Alta; Kusnawi, Kusnawi
The Indonesian Journal of Computer Science Vol. 12 No. 6 (2023): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v12i6.3567

Abstract

Rapid developments in voice-based emotion recognition have made positive contributions to human-computer interaction. This research aims to compare the performance of two algorithms, namely Multilayer Perception (MLP) and Support Vector Machine (SVM), in recognizing emotions based on sound. The data used in this research was taken from Kaggle, which amounted to 1440 voice data. The data is then collected into several emotions which will then be feature extracted from the dataset to eliminate irrelevant information and reduce noise so that the classification results are optimal. The research results show that the classification accuracy using the Multilayer Perception (MLP) algorithm reaches 83%, while the Support Vector Machine (SVM) reaches 82%. Based on the accuracy results of both methods, it can be concluded that the Multilayer Perception algorithm is superior to the Support Vector Machine algorithm in the context of voice-based emotion recognition. Keyword: Emotional Expression, Voice, Mfcc, Multi-Layer Perceptron, Support Vector Machine
Pemanfaatan Analisis Sentimen Terhadap Kasus Bunuh Diri Mahasiswa Menggunakan Naïve Bayes Classifier Ainnur Rafli; Kusnawi, Kusnawi
The Indonesian Journal of Computer Science Vol. 13 No. 1 (2024): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i1.3605

Abstract

Suicide is currently a serious problem in higher education, especially among university students, and special approaches and attention are required to prevent it. With today's advances in technology, emotion analysis techniques can be an effective way to understand students' feelings and thoughts that may lead to suicidal behavior or indicate a risk of suicide. For this study, we scraped the data for his 1,151 tweets on Twitter and cleaned it up to 817. Of these, there are 745 negative tweets and 72 positive tweets. Additionally, the data is implemented in an algorithm that performs a data split of 80:20 with an accuracy of 90,24%. That's the "depression" that often appears when visualizing Lata data. Especially in Indonesia, there are many suicides due to depression. The purpose of this study is to understand the factors associated with student suicide and to determine the effectiveness and accuracy of this algorithm. Additionally, this study is expected to provide insights into educational and mental health settings to improve prevention strategies and more effective approaches
Real-Time Explainable Concept Drift Detection for Eco-Driving in Mining Trucks using KSWIN and Event-Triggered SHAP Kusnawi; Mochamad Agung Wibowo; Ridwan Sanjaya
Journal of Information System and Informatics Vol 8 No 2 (2026): April
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i2.1551

Abstract

Fuel consumption represents a significant operational cost in mining, where real-time eco-driving optimization is hindered by dynamic and non-stationary operating conditions. Variations in operator behavior and environmental factors often induce concept drift, which diminishes the reliability of static machine learning models and constrains the effectiveness of conventional drift detection methods. This study proposes a distribution-aware, event-triggered Explainable Artificial Intelligence (XAI) framework for detecting and diagnosing fuel consumption anomalies in streaming telematics data. A Hoeffding Tree Regressor was evaluated using a prequential scheme on 1,927,867 real-world observations, achieving a Mean Absolute Error (MAE) of 19.43 under non-stationary conditions. Concept drift was monitored using the Kolmogorov–Smirnov Windowing (KSWIN) algorithm, which detected 1,874 drift events. Upon detection, an event-triggered SHAP module identified contributing factors, indicating that behavioral features such as engine speed and accelerator position were dominant contributors in early drift events. The primary contribution of this study is the integration of distribution-based drift detection with event-triggered explainability within a unified streaming framework, facilitating both anomaly detection and interpretable root-cause analysis.
Sentiment Analysis and Classification of Forest Fires in Indonesia Irawanto, Indra; Widodo, Cynthia; Hasanah, Atin; Dharma Kusumah, Prema Adhitya; Kusrini, Kusirini; Kusnawi, Kusnawi
ILKOM Jurnal Ilmiah Vol 15, No 1 (2023)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v15i1.1337.175-185

Abstract

Twitter is a well-known social media platform since it allows users to retweet, leave comments, exchange the latest information, and even find out about forest fires. However, no one has processed Twitter data in the form of the topic of forest fires. Despite the fact that this information is incredibly important for determining how much people care about sharing this knowledge and this phenomenon. Hence, one of the efforts in managing Twitter data in the form of text is using NLP (Natural Language Processing) which is now starting to be widely discussed. In addition, the use of word weighting utilizing Vader will also be used in this process. Furthermore, the use classifying process is conducted using 3 kinds of algorithms including Naïve Bayes, Random Forest and SVM (Support Vector Machine). The results of this study, the accuracy obtained from each method has not reached 90%. The Precision, Recall and F1-Score values have also not reached 90%.
Hybrid Food Recommendation System using Term Frequency–Inverse Document Frequency (TF-IDF), K-Nearest Neighbors (KNN), and Tag-Based Similarity Juventania Sheva Mellany; Kusnawi Kusnawi
Aviation Electronics, Information Technology, Telecommunications, Electricals, and Controls (AVITEC) Vol 8, No 1 (2026): February
Publisher : Institut Teknologi Dirgantara Adisutjipto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28989/avitec.v8i1.3683

Abstract

The rapid growth of the digital culinary industry increases the need for intelligent menu recommendation systems that can assist customers in making accurate and personalized choices. This study develops a hybrid food recommendation system that integrates three complementary approaches: popularity-based ranking, Term Frequency–Inverse Document Frequency (TF-IDF) with K-Nearest Neighbors (KNN) item similarity, and tag-based cosine matching. The system also incorporates a Content-Based Filtering component that leverages cosine similarity to strengthen similarity modeling across textual and tag-based representations. A total of 77,157 real transaction records from SR Cipali Restaurant, collected between April and December 2024, were used as the primary data source for system development and evaluation. Data preprocessing includes cleaning, category filtering, TF-IDF transformation for product names, One-Hot Encoding for tags, and price normalization to generate structured and comparable feature representations. Experimental results show that the TF-IDF KNN model achieves the best performance with an accuracy of 0.94, recall of 1.00, and F1-score of 0.89. The popularity-based model reaches an accuracy of 0.89 with balanced precision and recall of 0.80, while the tag-based model obtains a precision of 1.00 but lower recall due to tag inconsistency and ranking selectivity. The novelty of this study lies in the use of a hybrid lightweight framework evaluated on real-world restaurant transactions, which is rarely explored in previous research dominated by benchmark datasets. The proposed system demonstrates strong practicality for small and medium-sized restaurants that lack rating data and can be further improved by enhancing tag quality and incorporating more product attributes.
Co-Authors Abdulloh, Ferian Fauzi Afrig Aminuddin Agung Susanto Agung Susanto Ahmad Fauzi Ahmad Yusuf Ainnur Rafli Ainul Yaqin Aldi Yogie Pramono Ali Mustopa, Ali Alva Hendi Muhammad Andi Sunyoto Anggit Dwi Hartanto Anggit Dwi Hartanto, Anggit Dwi Ardiansyah, Fachri Arief Maehendrayuga Arief Setyanto Arifuddin, Danang Arnila Sandi Aryawijaya Asadulloh, Bima Pramudya Assani, Moh. Yushi Atin Hasanah Atmoko, Alfriadi Dwi Aulya, Fiola Utri BAYU SATRIYA, RIYAN Bhahari, Rifqi Hilal Candra Rusmana Christa Putri Rahayu Dede - Sandi Dede Husen Dede Sandi Dewi Kartika Dharma Kusumah, Prema Adhitya Dimaz Arno Prasetio Elsa Virantika Ema Utami Erna Utami Fajar Abdillah, Moh Fajar Aji Prayoga Hakiki, Muhammad Ridhwan Haris, Ruby Hartatik Haryo, Wasis Hasanah, Atin Hasirun Hasirun Hendrik Hendrik Henri Kurniawan Hidayatunnisa'i Huda, Luthfi Nurul Husni Hidayat Malik Indana Zulfa Indra Surya Permana Irawanto, Indra Joang Ipmawati Joang Ipmawati Joang Ipmawati Juventania Sheva Mellany Karisma Septa Kresna Karisma Septa Kresna Khairullah, Irfan Khalil Khoerul Anam, Khoerul Khoirunnita, Aulia Khrisna Irham Fadhil Pratama Kusrini Kusrini, Kusirini Ledyvia Audiz Coranov M Andika Fadhil Eka Putra M. Nurul Wathani Majid Rahardi Malik, Husni Hidayat Maringka, Raissa Mashuri, Ahmad Sanusi Melcior Paitin Kanoena Mochamad Agung Wibowo Mochamad Agung Wibowo Muh. Syarif Hidayatullah Muhammad Firdaus Abdi Muhammad Firdaus Abdi Muhammad Husein Budiraharjo Muhammad Irvan Shandika Muhammad Irvan Shandika Muhammad Reza Riansyah Nayoma, Fisan Syafa Neni Firda Wardani Tan Ngaeni, Nurus Sarifatul Nurul Zalza Bilal Jannah Olajuwon, Sayyid Muh. Raziq Omar Muhammad Altoumi Alsyaibani Pandiangan, Van Daarten Pebri Antara Pitaloka, Nadhira Triadha Prastyo, Rahmat Pringandana, Cokorda Gde Lanang Puji Prabowo, Dwi Qurniaty, Charlen Alta Raffa Nur Listiawan Dhito Eka Santoso Raffa Nur Listiawan Dhito Eka Santoso RAMADHAN, SYAIFUL Ridwan Sanjaya Ridwan Sanjaya Rifda Faticha Alfa Aziza Rita Wati Ritham Tuntun RIYAN BAYU SATRIYA Rizal Khadarusman Rodney Maringka Rohim, Ni’matur saifulloh Saifulloh, saifulloh Salman Alfaris Salman Alfaris, Salman San Sudirman Sekarsih, Fitria Nuraini Sentoso, Thedjo Sepriadi - Bumbungan Sepriadi Bumbungan Sri Yanto Qodarbaskoro Sry Faslia Hamka Sudirman, San Suyatmi Suyatmi Suyatmi Suyatmi Syaiful Huda Syaiful Ramadhan Tamuntuan, Virginia Taryoko, Taryoko Tegar Wirawan Teguh Arlovin Wahyu Pujiharto, Eka Wangsa, Sabda Sastra Wibowo , Mochamad Agung Widodo, Cynthia Widyanto, Agung Wirawan, Tegar Yudha Bagas Pattimura Yusa, Aldo Yusrinnatul Jinana triadin Yuza, Adela Zaenul Amri