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All Journal International Journal of Electrical and Computer Engineering Bulletin of Electrical Engineering and Informatics Bulletin of Electrical Engineering and Informatics JUITA : Jurnal Informatika Register: Jurnal Ilmiah Teknologi Sistem Informasi Bulletin of Electrical Engineering and Informatics Sistemasi: Jurnal Sistem Informasi Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Informatika Jurnal Teknik Komputer AMIK BSI Jurnal Khatulistiwa Informatika Paradigma Ekspektra: Jurnal Bisnis & Manajemen JITK (Jurnal Ilmu Pengetahuan dan Komputer) MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer SEINASI-KESI International Journal for Educational and Vocational Studies Jurnal Mantik Jurnal Teknik Informatika C.I.T. Medicom Journal of Intelligent Decision Support System (IDSS) Jurnal Bumigora Information Technology (BITe) Akrab Juara : Jurnal Ilmu-ilmu Sosial Jurnal Sistem Informasi IAIC Transactions on Sustainable Digital Innovation (ITSDI) Lumbung Inovasi: Jurnal Pengabdian Kepada Masyarakat Journal Software, Hardware and Information Technology International Journal of Basic and Applied Science Reputasi: Jurnal Rekayasa Perangkat Lunak Jurnal Sains Informatika Terapan (JSIT) INTERNATIONAL JOURNAL OF MECHANICAL COMPUTATIONAL AND MANUFACTURING RESEARCH Paradigma Indonesian Journal Computer Science (ijcs) Jurnal MENTARI: Manajemen, Pendidikan dan Teknologi Informasi International Journal of Enterprise Modelling Jurnal Teknoinfo
Mochamad Wahyudi
Universitas Bina Sarana Informatika
Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Physics Social Sciences Transportation Other

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Deteksi dan Prediksi Cerdas Penyakit Paru-Paru dengan Algoritma Random Fores Kurniawan, Deny; Wahyudi, Mochamad; Pujiastuti, Lise; Sumanto, Sumanto
Indonesian Journal Computer Science Vol. 3 No. 1 (2024): April 2024
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/ijcs.v3i1.6071

Abstract

Penyakit paru-paru, seperti COPD, kanker paru-paru, dan asma, adalah masalah kesehatan global yang menyebabkan lebih dari tujuh juta kematian setiap tahun. Teknologi canggih, termasuk model deep learning dan algoritma Random Forest, telah digunakan secara efektif untuk mendeteksi dan mengklasifikasikan penyakit paru-paru dari data pencitraan dengan akurasi tinggi. Penelitian ini bertujuan menunjukkan efektivitas algoritma Random Forest dalam memprediksi penyakit paru-paru. Dataset yang digunakan terdiri dari 30.000 data dengan 11 atribut, diperoleh dari Kaggle dan diproses menggunakan perangkat lunak Orange versi 3.36.2. Algoritma Random Forest diimplementasikan dengan 10 pohon keputusan dan enam atribut yang dipertimbangkan pada setiap pembagian data. Model ini diuji menggunakan validasi silang dengan 10 lipatan, dan hasil pengujian menunjukkan nilai AUC sebesar 0,993, yang mengindikasikan tingkat akurasi yang sangat tinggi. Matriks kebingungan digunakan untuk mengevaluasi kinerja model, dengan mengukur akurasi, presisi, recall, F1-Score, dan AUC. Model ini menunjukkan akurasi yang tinggi, dengan nilai ROC AUC 0,453 untuk prediksi adanya penyakit paru-paru dan 0,547 untuk prediksi ketiadaan penyakit paru-paru. Hasil ini menunjukkan bahwa algoritma Random Forest dapat menjadi alat yang efektif dalam mengidentifikasi penyakit paru-paru. Penelitian ini berkontribusi pada pengembangan teknik diagnostik yang lebih akurat dan efisien, yang dapat membantu tenaga medis dalam mendiagnosis penyakit paru-paru pada pasien. Dengan pemahaman yang lebih baik tentang penerapan algoritma ini dalam dunia kesehatan, diharapkan dapat meningkatkan kualitas diagnosis dan perawatan pasien secara signifikan.
Advanced graph neural networks for dynamic yield optimization and resource allocation in industrial systems Pujiastuti, Lise; Wahyudi, Mochamad
Jurnal Teknik Informatika C.I.T Medicom Vol 16 No 2 (2024): May: 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.Vol16.2024.785.pp90-102

Abstract

This research explores the integration of Graph Neural Networks (GNNs) and Reinforcement Learning (RL) for dynamic yield optimization and resource allocation in industrial systems. We present a numerical example involving a small manufacturing setup with three machines, where GNNs are employed to model complex interactions and derive meaningful embeddings of machine states. These embeddings are then used to predict yield and cost through linear combination functions. RL is utilized to optimize resource allocation dynamically, balancing yield and cost through a carefully designed reward function. The results demonstrate the effectiveness of GNNs in capturing machine interactions and the adaptability of RL in optimizing operational parameters in real-time. This combined approach showcases significant potential for enhancing efficiency, cost-effectiveness, and overall performance in various industrial applications, providing a robust framework for continuous improvement and adaptive decision-making in dynamic environments.
IMPLEMENTATION OF K-MEDOIDS METHOD FOR HEART DISEASE PREDICTION USING QUANTUM COMPUTING AND MANHATTAN DISTANCE Mochamad Wahyudi; Dimas Trianda; Lise Pujiastuti; Solikhun Solikhun
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 10 No. 3 (2025): JITK Issue February 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v10i3.5637

Abstract

Heart disease is a severe health condition characterized by dysfunctions in the heart and blood vessels, which can be fatal if not properly managed. Early detection and prediction of heart disease are crucial for understanding the prevalence and determining patients' quality of life. In this study, quantum computing is applied to enhance the performance of the K-Medoids method. A comparative analysis of these methods is conducted, focusing on their performance. The investigation utilizes a dataset of heart disease patient medical records. This dataset includes various attributes used to predict heart disease patterns. The dataset is tested using both the classical and K-Medoids methods with a quantum computing approach, employing Manhattan distance calculations. This study's findings reveal that applying quantum computing to the K-Medoids method results in clustering accuracy stability of 85%, equivalent to the classical method. Although there is no increase in accuracy, the quantum computing approach demonstrates potential improvements in data processing efficiency. These results highlight that the K-Medoids method with a quantum computing approach can contribute significantly to faster and more efficient medical data analysis. However, further research is needed for optimization and testing on more extensive and more diverse datasets.
Comparison of Manhattan and Chebyshev Distance Metrics in Quantum-Based K-Medoids Clustering Solikhun, Solikhun; Siregar, Muhammad Rahmansyah; Pujiastuti, Lise; Wahyudi, Mochamad; Kurniawan, Deny
Sistemasi: Jurnal Sistem Informasi Vol 14, No 4 (2025): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v14i4.5193

Abstract

Anemia is a condition characterized by a decrease in the number of red blood cells or hemoglobin levels in the bloodstream. It can lead to fatigue and reduced productivity. Clustering is a technique in data mining used to identify patterns that can support decision-making processes. In the case of anemia, clustering plays a crucial role in identifying various severity patterns and understanding the contributing factors behind the condition. Quantum computers, which utilize the principles of quantum mechanics for information processing, have made significant advancements over the past decade. Quantum computing is an advanced method of information processing that leverages qubits, enabling systems to exist in multiple states simultaneously. This technology offers the potential to solve complex problems at exponentially faster speeds than classical computers. In this study, researchers applied the K-Medoids clustering algorithm, calculated using quantum-based equations. The research compares two distance measurement methods: Chebyshev distance and Manhattan distance. The results show that the Manhattan algorithm performs better in medical contexts, particularly for detecting positive cases, with a recall of 0.57 and an F1-score of 0.695, although it has a slightly lower precision of 0.88. This makes it more suitable for medical applications where false negatives carry high risks, such as disease detection, despite its higher cost and mean squared error (MSE). On the other hand, Chebyshev distance achieved perfect precision (1.0) and higher accuracy (80%), but its low recall (0.33) indicates that many positive cases were missed. Therefore, Manhattan distance is more recommended for medical applications that require the detection of more positive cases, while Chebyshev is more efficient for scenarios that prioritize accuracy and cost.
Manhattan Distance-based K-Medoids Clustering Improvement for Diagnosing Diabetic Disease Solikhun; Rahmansyah Siregar, Muhammad; Pujiastuti, Lise; Wahyudi, Mochamad
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 6 (2024): December 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i6.5894

Abstract

Diabetes is a metabolic disorder characterized by blood glucose levels above normal limits. Diabetes occurs when the body is unable to produce sufficient insulin to regulate blood sugar levels. As a result, blood sugar management becomes impaired and there is no cure for diabetes. Early detection of diabetes provides an opportunity to delay or prevent its progression into acute stages. Clustering can help identify patterns and groups of diabetes symptoms by analyzing attributes that indicate these symptoms. In this study, researchers are using K-Medoid and Quantum K-Medoid methods for clustering diabetes data. Quantum computing utilizes quantum bits, or qubits, which can represent multiple states at the same time. Compared to classical computers, quantum computing has the potential for an exponential speedup in problem-solving. Researchers conducted a comparison between two methods: the classic K-Medoids method and the K-Medoids method utilizing quantum computing. The researchers found that both Quantum K-Medoid and Classic K-Medoid achieved the same clustering accuracy of 91%. In testing with the Quantum K-Medoids algorithm, it was found that the cost value in the 8th epoch showed a significant decrease compared to the Classical K-Medoids algorithm. This demonstrates that Quantum K-Medoid can be considered a viable alternative for clustering purposes.
Analisa Pola Penyebaran Pengguna Layanan Transjakarta dengan Metode K-Means Clustering Reynaldi , Reynaldi; Faiz Djarot, Raihan Jamal; Wahyudi, Mochamad; Sumanto , Sumanto; Budiman, Ade Surya
Journal Software, Hardware and Information Technology Vol 5 No 2 (2025)
Publisher : Jurusan Sistem Informasi Universitas Islam Negeri Alauddin Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24252/shift.v5i2.205

Abstract

This study analyzes the spatial distribution patterns of Transjakarta service users in Jakarta using the K-Means Clustering algorithm. The dataset, obtained from the Kaggle platform, consists of 189,501 passenger transaction records, including tap-in and tap-out locations, travel times, and user-related information. The research process involves data collection, preprocessing to remove missing values, application of the K-Means Clustering algorithm, and determination of the optimal number of clusters using the elbow method. Based on the analysis, the optimal number of clusters is identified as four (K=4). A scatter plot visualization presents user distribution patterns based on geographic coordinates and service usage times. Each cluster represents a group of users with similar travel characteristics. This analysis results in a segmentation that reflects variations in Transjakarta passenger mobility patterns and illustrates how travel activity is distributed across spatial and temporal dimensions within the urban area of Jakarta.
Effect of Work Team, School Culture on Decision Making of The Principals of The State High School in Jakarta Oktaviany, Venny; Mukhtar, Mukhneri; Wahyudi, Mochamad
International Journal for Educational and Vocational Studies Vol. 1 No. 4 (2019): August 2019
Publisher : Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/ijevs.v1i4.1609

Abstract

The objective of this study is to learn the influence of work team and school culture toward decision making of high school in Jakarta. This study was the quantitative approach, and  path analysis method was applied to analyze the data. In this study, principals of state high school have been chosen as a unit analysis and 91 samples of state high school in Jakarta. The results of this study showed that: (1) work teams have a direct positive effect on decision making; (2) school culture has a direct positive effect on decision making. At last, this study suggested that work teams and school culture were important determinants of the principals decision making.
Inclusive Education: Cooperation Between Class Teachers, Special Teachers, Parents to Optimize Development of Special Needs Childrens Ariani, Alpha; Wahyudi, Mochamad; Rugaiyah, Rugaiyah
International Journal for Educational and Vocational Studies Vol. 1 No. 5 (2019): September 2019
Publisher : Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/ijevs.v1i5.1616

Abstract

This study aims to find out the collaboration between class teachers, special guidance teachers, and parents of children with special needs. In addition, it also knows the optimization of the developmental aspects of children with special needs which include academic attitudes, social skills, emotions, and independence. This study used qualitative research methods. The process of data collection is done using the method of interviews, observation, and documentation study. Data analysis using the model presented by Miles and Huberman includes the process of data reduction, data presentation, and drawing conclusions based on facts in the field. The results of the study show that good collaboration between classroom teachers, special guidance teachers, and parents in the process of education in schools strongly supports the development of children with special needs. The development of aspects of social skills is more prominent than the development of aspects of academic attitudes, emotional development, and independence.
Development of Information Systems Models Management for Students Ikhwan, Subaiki; Mukhtar, Mukhneri; Wahyudi, Mochamad
International Journal for Educational and Vocational Studies Vol. 1 No. 7 (2019): November 2019
Publisher : Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/ijevs.v1i7.1775

Abstract

Student management is an important part that must be considered by schools. The government has made standardized rules for how schools can manage students so that they are in accordance with National Education Standards. Student management activities start from registration, until student services that have been arranged neatly, may not run optimally, if schools are still manual in tidying student data collection. By him the role of student management information systems is very important for every school to have. This study aims to determine how schools can create student management information systems that are feasible and effective.
Perbandingan Algoritma Random Forest, Naive Bayes, Dan Neural Network Dalam Klasifikasi Penyakit Jantung Rani, Maulidina Cahaya; Dewi, Revinta Arrova; Azkia, Farah Diba; Wahyudi, Mochamad; Sumanto; Budiman, Ade Surya
Jurnal Sains Informatika Terapan Vol. 4 No. 2 (2025): Jurnal Sains Informatika Terapan (Juni, 2025)
Publisher : Riset Sinergi Indonesia (RISINDO)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62357/jsit.v4i2.609

Abstract

Penyakit jantung merupakan salah satu masalah kesehatan paling mematikan di dunia, dengan jumlah kematian yang terus meningkat setiap tahunnya. Penyakit kardiovaskular adalah penyebab utama kematian di seluruh dunia, dengan lebih dari 17 juta kematian setiap tahun, menurut data WHO. Gangguan fungsi jantung ini dapat dipicu oleh berbagai faktor risiko seperti pola makan tidak sehat, obesitas, kurang aktivitas fisik, kebiasaan merokok, dan riwayat penyakit dalam keluarga. Oleh karena itu, deteksi dini sangat penting untuk mencegah dan mengurangi risiko kematian akibat penyakit jantung. Penelitian ini bertujuan untuk membandingkan performa tiga metode klasifikasi, yaitu Random Forest, Neural Network, dan Naive Bayes dalam mengklasifikasi risiko penyakit jantung. Pengujian model dilakukan menggunakan metode Random Sampling dengan skema repeat train/test sebanyak 10 kali, di mana setiap iterasi menggunakan 80% data sebagai training set. Berdasarkan hasil evaluasi, model Random Forest menghasilkan nilai AUC sebesar 0,996, model Naive Bayes sebesar 0,980, dan model Neural Network sebesar 0,957. Selain itu, analisis dilakukan untuk menilai keunggulan dan kelemahan masing-masing metode dalam menangani data dengan fitur yang kompleks dan saling berkorelasi. Hasil penelitian ini diharapkan dapat memberikan rekomendasi metode klasifikasi yang paling efektif dan efisien untuk diterapkan dalam sistem pendukung keputusan medis guna deteksi dini penyakit jantung.
Co-Authors Abdurrachman, Qais Ade Budiman, Ade Adi Supriyatna Akbar, Habibullah Ali Haidir Alpha Ariani, Alpha Andri Amico Atrinawati, Lovinta Happy Azis, Munawar Abdul Azkia, Farah Diba Barreto Jose da Conceição Budiman, Ade Surya Dedi Triyanto Dedi Triyanto Dedi Triyanto Deni Kurniawan, Deni Dennis Gunawan, Dennis Deny Kurniawan DENY KURNIAWAN Dewi, Revinta Arrova Dimas Trianda Doni Purnama Alam Syah, Doni Purnama Dwi Arum Ningtyas Efendi, Syahril Faiz Djarot, Raihan Jamal Fajar Akbar Firmansyah Firmansyah Firmansyah Firmansyah Firmansyah Firmansyah Firmansyah Firmansyah Firmansyah Freshtiya Beby Larasati Fristi Riandari Fuad Nur Hasan Ganda Wijaya Ganda Wijaya, Ganda Givan, Bryan Hartama, Dedy Hengki Tamando Sihotang Herman Mawengkang Husain Husain Husain Husain Ihsan Daulay Ikhwan, Subaiki Imam Sutoyo Indra Chaidir, Indra Khoirun Nisa KHOIRUN NISA Kotjek, Rafie Laksono, Andriansyah Tri Lestari Yusuf Lise Pujiastuti Lise Pujiastuti Lise Pujiastuti Lise Pujiastuti Lise Pujiastuti Lise Pujiastuti Lise Pujiastuti Lise Pujiastuti Merio Hengki Muhammad Safii Muhammad Zarlis Mukhtar, Mukhneri Noviyanto Nurajijah Nurajijah Nurhasanah Halim Oktaviany, Venny Pricillia Pujiastuti , Lise Pujiastuti, Lise Rachmat Adi Purnama Rahmansyah Siregar, Muhammad Rani, Maulidina Cahaya Retno Dwigustini Reynaldi , Reynaldi Rifani Haikal Riska Aryanti Riski Wulandari Rugaiyah Safii Safii Sfenrianto Sfenrianto Siregar, Muhammad Rahmansyah Solikhun Solikhun Solikhun Solikhun Solikhun Solikhun Solikhun Solikhun Solikhun Solikhun Solikhun Solikhun Solikhun Solikhun Solikhun Solikhun Solikhun, Solikhun SUMANTO Sumanto Sumanto Sumanto, Sumanto Sunu Sugi Arso Susilawati Susilawati Sutarman Sutarman Syarifah Putri Agustini Tantrisna, Ellen Vinsensia, Desi Wijaya, Filzah Yahya Mara Ardi Yosua Chandra Simamora Yudha, Satria Wira Yuni Eka Achyani, Yuni Eka Zidan, Muhammad