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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 Indonesian Journal of Business Intelligence (IJUBI) bit-Tech Aviation Electronics, Information Technology, Telecommunications, Electricals, 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 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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INOVASI PENDIDIKAN MASA DEPAN DENGAN PEMANFAATAN TEKNOLOGI INFORMASI UNTUK MENINGKATAN KUALITAS PENDIDIKAN ANAK USIA DINI DI PKG PAUD GODEAN YOGYAKARTA Atmoko, Alfriadi Dwi; Yaqin, Ainul; Kusnawi
Jurnal Abdi Insani Vol 11 No 1 (2024): Jurnal Abdi Insani
Publisher : Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/abdiinsani.v11i1.1199

Abstract

Pendidikan Anak Usia Dini (PAUD) in Indonesia is an important foundation for building a better future generation. PAUD in Indonesia is currently growing rapidly because awareness of the importance of education grows from an early age. Based on reference data from the Central Statistics Agency (BPS) and the Ministry of Education and Culture (Kemendikbud), the number of children attending PAUD in Indonesia will increase to 11.6 million in 2022, and the proportion of enrollment of children aged 4 to 6 years will be 74.3% . According to article 1.14 in the Law of the Republic of Indonesia No. 20 of 2003, PAUD education plays a very important role in the development of a child's personality and prepares them for the transition to the next level of education. PAUD implementation in Godean sub-district is under the Kapanewon Godean Yogyakarta Cluster Activity Center (PKG) where there are 61 members. In terms of implementing PAUD, it is hoped that the preparation of learning, monitoring and evaluation and management of learning activities can achieve good quality PAUD education, but in practice it has been supported by computers but not yet supported by information technology. The method of implementing this service is by providing training in Utilization of Google Workspace features and filling out online report cards for PAUD and FGD students.
Analisis Rekomendasi untuk Meningkatkan Nilai Capability Level Domain APO 14 Pada COBIT 2019 Taryoko, Taryoko; Muhammad, Alva Hendi; Kusnawi, Kusnawi
Jurnal Ilmiah Universitas Batanghari Jambi Vol 24, No 1 (2024): Februari
Publisher : Universitas Batanghari Jambi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33087/jiubj.v24i1.4380

Abstract

The purpose of this study was to determine data management with consideration of the APO 14 domain at XYZ Agencies using the 2019 COBIT Framework. This research method uses a case study. The results of this study indicate that first, the capability level test value is entered at level 3, namely Establish. Second, the average value generated on the Capability level test value is 3.14 or 0.031, which means the XYZ agency So that it can be ensured that the XYZ agency has carried out the implementation process and is able to achieve process results in accordance with what is targeted in the APO domain 14. Third, the average GAP value produced is worth 3 with a difference of 1 value from the expected value in accordance with the 2019 COBIT provisions.
A Systematic Literature Review of Adaptive Machine Learning Approaches for Real-Time Fuel Efficiency Optimization in Open-Pit Mining Trucks Kusnawi; Mochamad Agung Wibowo; Ridwan Sanjaya
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 15 No. 01 (2026): JANUARY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v15i01.2527

Abstract

Fuel consumption in open-pit mining operations is a significant operational cost, making fuel efficiency an important research topic. This project seeks to investigate the use of adaptive machine learning (ML) methodologies to improve real-time fuel efficiency in mining trucks. A Systematic Literature Review (SLR) was conducted following the PRISMA protocol to examine 47 peer-reviewed articles published from 2015 to 2025. Thematic synthesis and bibliometric analysis identified five dominant categories of machine learning, with deep learning and fuzzy logic being the most common. Many studies have examined adaptive energy regulation for varying terrain and loads; however, only 20% have included driver behavior, highlighting a significant research gap. Reinforcement learning and hybrid systems show significant potential for scheduling and control in dynamic environments; however, they face challenges in real-time applications due to factors such as edge computing and limited data integration. This review describes advances in fuel optimization research through the integration of artificial intelligence, control theory, and mining logistics, and proposes future goals including the development of simplified models for vehicle applications, empirical testing in industrial fleets, and the utilization of behavior and telemetry data to enhance contextual awareness in systems. Additionally, future research should focus on the real-time integration of driver behavior into adaptive ML models and the development of lightweight, deployable solutions tailored for industrial-scale applications in mining fleets.
Sentiment Analysis of Neobank Digital Banking using Support Vector Machine Algorithm in Indonesia Kusnawi, Kusnawi; Rahardi, Majid; Pandiangan, Van Daarten
JOIV : International Journal on Informatics Visualization Vol 7, No 2 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.2.1652

Abstract

Currently, in the industrial era 4.0, information and communication technology is very developed, whereas, in this era, there is an increase in complex activities, one of which is in the banking sector. With the ease and efficiency of online finance, people want to switch to using digital banks. Neobank is an online savings and deposit application from Bank Neo Commerce (BCN) that the public can use by using the Internet. One of the online services is mobile banking which can be used by both Android and iOS versions of customers. Users can review Neobank's performance and services through the Google Play Store to improve and evaluate Neobank's performance. Neobank application reviews on the Google Play Store are increasing. Therefore, a review analysis is needed by conducting a sentiment analysis on Neobank's review. The data amounted to 3159 user reviews collected from reviews of the Neobank application on the Google Play Store. This study aims to classify Neobank user review data, including positive or negative sentiments. The method used in this study is an experimental method using the Support Vector Machine algorithm. The accuracy results obtained using the Support Vector Machine algorithm are 82.33%, which is owned by the scenario of 90% training data and 10% test data. The precision results are 82%, and recall is 81%. Future studies can add datasets from various sources so that there are even more datasets so as to increase the accuracy of model classification.
MSME AI Readiness Analysis Using The AIRI Framework: Analisis Kesiapan AI UMKM Menggunakan Kerangka Kerja AIRI Muhammad Husein Budiraharjo; Alva Hendi Muhammad; Kusnawi
Jurnal Teknologi Informasi Universitas Lambung Mangkurat (JTIULM) Vol. 9 No. 2 (2024)
Publisher : Fakultas Teknik Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/jtiulm.v9i2.307

Abstract

AI is expected to become one of the key technologies supporting the development of MSMEs, which represent a major pillar of Indonesia's economy. Successful adoption and implementation of AI require the right strategies, one of which stems from an analysis of a company’s AI readiness. In this study, an AI readiness analysis was conducted using the AIRI framework on six MSMEs from various business sectors. The results of the analysis provided the AI readiness levels of each MSME, along with comparisons to similar industries and to industries of comparable business scale (MSME). The analysis also yielded several recommendations for AI adoption and strategies to enhance the AI readiness of each MSME. All the MSMEs involved in the study positively accepted the AI readiness analysis and the adoption recommendations provided. The study did not produce any feedback for improvements to the AIRI framework itself; however, there were suggestions for further development of the AIRI application to better assist MSMEs in determining AI readiness targets and appropriate AI implementation strategies in the future..
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.
IMPLEMENTASI MOORA PADA SELEKSI DOSEN TERBAIK BERDASARKAN HASIL PENILAIAN DALAM PEMBELAJARAN KULIAH Hasirun, Hasirun; Kusrini, Kusrini; Kusnawi, Kusnawi
Indonesian Journal of Business Intelligence (IJUBI) Vol 6 No 1 (2023): Indonesian Journal of Business Intelligence (IJUBI)
Publisher : Universitas Alma Ata

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21927/ijubi.v6i1.3331

Abstract

Lecturer performance assessment is one of the activities of monitoring and evaluating performance with the aim of supervising the learning process and ensuring that lecturers carry out their duties in accordance with policies and teaching materials that have been determined. Lecturer performance assessment is carried out by students at the end of each semester by assessing lecturers based on criteria related to lecture learning. The criteria assessed in college learning are learning aspects, technological proficiency, integrity, and inspiration. The results of the student assessment will be reported to the learning development and quality assurance institution, which can later be used to determine the best lecturer performance. In this research, we apply the MOORA method to help determine the best lecturer based on assessment results in lecture reasoning. In its implementation, the MOORA method performs calculations based on criteria and weight values that have been determined and produces a ranking that can be used to determine the best lecturer's performance. In this study, the highest ranking was on the VPB alternative with a final value of 0.138, while the lowest value was on the DAM alternative with a final value of 0.108.
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.
Integration of K-Means Clustering, Random Forest, and RFM Analysis for Optimizing Consumer Segmentation in Digital Advertising Strategies Ipmawati, Joang; Kusnawi, Kusnawi
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 15 No. 01 (2026): JANUARY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v15i01.2548

Abstract

In the era of data-driven marketing, accurate consumer segmentation is essential to improve the precision and impact of digital advertising. This study aims to produce more accurate consumer segmentation to support more targeted digital marketing strategies. The methods used include K-Means Clustering to group users based on digital behavior, RFM Analysis to evaluate user loyalty and interaction value with advertisements, and Random Forest to identify key factors influencing segmentation. The dataset includes demographic and behavioral information such as age, gender, income level, online duration, and interaction with digital ads. This dataset includes 200 user samples collected from public online advertising platforms. The results show that using five clusters (K=5) in K-Means Clustering yields optimal segmentation. RFM Analysis successfully categorizes users based on loyalty and engagement, while Random Forest identifies Click-Through Rate (CTR), Likes and Reactions, and Time Spent Online as the most influential variables in segmentation. This research contributes to improving the effectiveness of digital advertising campaigns and supports data-driven decision-making. The findings are significant for understanding consumer behavior patterns more deeply and for designing more efficient and relevant marketing strategies.
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.
Co-Authors Abdulloh, Ferian Fauzi Afrig Aminuddin Agung Susanto Agung Susanto Ahmad Fauzi Ahmad Yusuf Ainnur Rafli Ainul Yaqin Ali Mustopa, Ali Alva Hendi Muhammad Andi Sunyoto Anggit Dwi Hartanto, Anggit Dwi Ardiansyah, Fachri 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 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 Haris, Ruby Hartatik Haryo, Wasis Hasanah, Atin Hasirun Hasirun Hasirun, Hasirun Hendrik Hendrik Henri Kurniawan Hidayatunnisa'i Huda, Luthfi Nurul Irawanto, Indra Joang Ipmawati Kanoena, Melcior Paitin Karisma Septa Kresna Khairullah, Irfan Khalil Khoerul Anam, Khoerul Khoirunnita, Aulia Khrisna Irham Fadhil Pratama Kusrini Kusrini, Kusirini M Andika Fadhil Eka Putra M. Nurul Wathani Majid Rahardi Malik, Husni Hidayat Maringka, Raissa Mashuri, Ahmad Sanusi Mochamad Agung Wibowo Muh. Syarif Hidayatullah Muhammad Firdaus Abdi Muhammad Firdaus Abdi Muhammad Husein Budiraharjo Muhammad Irvan Shandika Muhammad Reza Riansyah Nayoma, Fisan Syafa Neni Firda Wardani Tan Ngaeni, Nurus Sarifatul Nurul Zalza Bilal Jannah Omar Muhammad Altoumi Alsyaibani Pandiangan, Van Daarten Pattimura, Yudha Bagas Pebri Antara Pitaloka, Nadhira Triadha Pramono, Aldi Yogie Prastyo, Rahmat Puji Prabowo, Dwi Qurniaty, Charlen Alta Raffa Nur Listiawan Dhito Eka Santoso Rahayu, Christa Putri RAMADHAN, SYAIFUL Ridwan Sanjaya Rifda Faticha Alfa Aziza Rita Wati Ritham Tuntun 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 Teguh Arlovin Wahyu Pujiharto, Eka Wangsa, Sabda Sastra Widodo, Cynthia Widyanto, Agung Wirawan, Tegar Yusa, Aldo Yusrinnatul Jinana triadin Yuza, Adela Zaenul Amri