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CLUSTERING TRAFO DISTRIBUSI MENGGUNAKAN ALGORITMA SELF-ORGANIZING MAP Khotimah, Tutik; Syukur, Abdul; Soeleman, M. Arief
Simetris: Jurnal Teknik Mesin, Elektro dan Ilmu Komputer Vol 8, No 1 (2017): JURNAL SIMETRIS VOLUME 8 NO 1 TAHUN 2017
Publisher : Universitas Muria Kudus

Salah satu cara untuk mengetahui beban sebuah trafo distribusi PLN masih memenuhi batas normal atau overload adalah dengan melakukan pengukuran beban trafo tersebut. Pada PLN Area Pelayanan Jaringan Kudus, pengukuran beban dilakukan baik pada siang hari mau pun pada malam hari. Hasil pengukuran tersebut memiliki kemungkinan berbeda. Hal ini disebabkan pada siang hari penggunaan beban cenderung kecil, sedangkan pada malam hari pemakaian beban lebih besar. Hal ini menyebabkan sulitnya menentukan beban trafo tersebut masih normal atau overload. Untuk memetakan beban trafo distribusi secara cepat dan akurat, diperlukan teknik data mining yaitu clustering. Penelitian ini dilakukan dengan menerapkan algoritma Self Organizing Map (SOM). Dengan SOM dihasilkan nilai akurasi sebesar 93% terhadap hasil pengukuran beban trafo distribusi pada siang hari dan sebesar 84% terhadap hasil pengukuran beban trafo distribusi pada malam hari. Sedangkan error yang dihasilkan dari pemetaan dengan SOM sebesar 7% terhadap hasil pengukuran beban trafo distribusi pada siang hari dan sebesar 16% terhadap hasil pengukuran beban trafo distribusi pada malam hari.

Enhancing Machine Learning Accuracy in Detecting Preventable Diseases using Backward Elimination Method Dliyauddin, Muhammad; Shidik, Guruh Fajar; Affandy, Affandy; Soeleman, M. Arief
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 1 (2024): Januari 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i1.7073

In the current landscape of abundant high-dimensional datasets, addressing classification challenges is pivotal. While prior studies have effectively utilized Backward Elimination (BE) for disease detection, there is a notable absence of research demonstrating the method's significance through comprehensive comparisons across diverse databases. The study aims to extend its contribution by applying BE across multiple machine learning algorithms (MLAs)Nave Bayes (NB), k-Nearest Neighbors (KNN), and Support Vector Machine (SVM)on datasets associated with preventable diseases (i.e. heart failure (HF), breast cancer (BC), and diabetes). This study aims to elucidate and recommend significant differences observed in the application of BE across diverse datasets and machine learning (ML) methods. This study conducted testing on four distinct datasetsraisin, HF, BC, and early-stage diabetes risk prediction datasets. Each dataset underwent evaluation with three MLAs: NB, KNN, and SVM. The application of BE successfully eliminated non-significant attributes, retaining only influential ones in the model. In addition, t-test results revealed a significant impact on accuracy across all datasets (p-value < 0.05). In specific algorithmic evaluations, SVM exhibited the highest accuracy for the raisin dataset at 87.22%. Additionally, KNN attained the utmost accuracy in the heart failure dataset with an accuracy of 86.31%. In the breast cancer dataset, KNN again excelled, achieving an accuracy of 83.56%. For the diabetes dataset, KNN proved the most accurate, reaching 96.15%. These results underscore the efficacy of BE in enhancing the execution of MLAs for disease detection.

Computer-Aided Diagnosis (CAD) of Stroke in The Brain CT-Scan Images Using Integration of Grey Level Co-Occurrence Matrix (GLCM) Texture Feature Extraction And K-Nearest-Neighbour (KNN) Classification Casidi, Casidi; Syukur, Abdul; Soeleman, M. Arief; Nurhindarto, Aris
Decode: Jurnal Pendidikan Teknologi Informasi Vol. 4 No. 3: NOVEMBER 2024
Publisher : Program Studi Pendidikan Teknologi Infromasi UMK

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51454/decode.v4i3.646

This study presents an advanced and efficient computer-aided diagnosis (CAD) system for stroke detection using brain CT images, integrating Grey Level Co-Occurrence Matrix (GLCM) feature extraction and K-Nearest Neighbour (KNN) classification. The objective is to enhance stroke detection accuracy and efficiency in clinical settings. A dataset of 400 brain CT images, divided into 300 for training and 100 for testing with equal normal and stroke classes, was used to evaluate performance. The GLCM texture features significantly differentiated between normal and stroke images. The optimized KNN model demonstrated high performance, achieving 99% classification accuracy, 100% sensitivity, 98% specificity, 97% precision, a 99% F1 score, 100% positive predictive value, and 98% negative predictive value. The average computation time per image was 3.2 seconds, indicating feasibility for real-time application. In conclusion, the GLCM-KNN integrated CAD system proves to be an accurate and efficient method for stroke diagnosis on brain CT scans, offering a potential solution for early stroke detection in resource-limited healthcare facilities.

Pelatihan Pembuatan Konten Multimedia untuk Terapan materi Video Pembelajaran di SMPN 2 Kledung Kabupaten Temanggung -, Muslih; Pramudya, Elkaf Rahmawan; Senoprabowo, Abi; Soeleman, M. Arief; Asfawi, Supriyono
ABDIMASKU : JURNAL PENGABDIAN MASYARAKAT Vol 7, No 2 (2024): MEI 2024
Publisher : LPPM UNIVERSITAS DIAN NUSWANTORO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/ja.v7i2.2203

Sekolah merupakan sebuah institusi yang bertanggungjawab untuk menyelenggarakan pendidikan dan salah satu hal yang menunjang dalam menyelenggarakan pendidikan adalah adanya media pembelajaran yang baik dan bervariatif. Salah satu sekolah yang ada di kabupaten Temanggung yaitu SMPN 2 Kledung. SMPN 2 Kledung menjadi salah satu lembaga pendidikan yang mengalami cukup kesulitan dalam mengembangkan media pembelajaran yang berkualitas khususnya video pembelajaran. Kegiatan pembelajaran SMPN 2 Kledung masih dilaksanakan secara konvensional. Kendala ketersediaan peralatan dan media pembelajaran yang berbasis TIK menjadi tantangan utama. Video pembelajaran yang digunakan di SMPN 2 Kledung didapatkan para guru dari youtube dan media sosial. Kendala yang dihadapi oleh para guru adalah karena video yang diambil dan bukan membuat sendiri, maka materi yang diberikan tidak semuanya sesuai dengan yang diinginkan oleh pengajar. Selain itu, kebanyakan materi tersedia dalam bahasa inggris yang akan sulit dipahami oleh peserta didik. Padahal video pembelajaran yang ada sangat menarik karena mengabungkan antara tampilan pembicara dan juga materi yang disampaikan. Target luaran kegiatan ini adalah Meningkatnya pemahaman peserta tentang membuat konsep video pembelajaran yang diterapkan dalam pembuatan naskah. Meningkatnya ketrampilan peserta tentang cara mengambil video pembelajaran yang baik dan menarik. Meningkatnya ketrampilan peserta tentang editing video, serta distribusi video yang sesuai untuk peserta didik. Rencana kegiatan yang akan dilakukan dengan cara memberikan materi dan praktik tentang membuat konsep video dan pembuatan naskah, memberikan materi dan praktik tentang teknik mengambil video, dan memberikan materi dan praktik tentang editing video dan menyiapkan video yang siap didistribusikan.

Improving Random Forest Performance for Sentiment Analysis on Unbalanced Data Using SMOTE and BoW Integration: PLN Mobile Application Case Study Rahmatullah, Muhammad Rifqi Fadhlan; Andono, Pulung Nurtantio; Affandy; Soeleman, M. Arief
Scientific Journal of Informatics Vol. 12 No. 1: February 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v12i1.19295

Purpose: This research aims to improve the accuracy of sentiment analysis on PLN Mobile app reviews by overcoming the challenge of data imbalance. This goal is important to provide a better understanding of user opinions and support PT PLN (Persero) in improving mobile application services. Methods: This research uses the Random Forest algorithm combined with Synthetic Minority Over-sampling Technique (SMOTE) to handle imbalanced data. Data is collected through web scraping reviews from the Google Play Store, followed by preprocessing processes such as data cleaning, stopword removal, tokenization, and stemming. Feature extraction is performed using the Bag of Words (BoW) method, and the data is tested with four sharing schemes. Result: The results showed that the 90%-10% sharing scheme gave the best performance with an accuracy of 81% and an average precision and recall of 0.79. This finding confirms that the larger the proportion of training data, the better the model performs sentiment classification. Novelty: This research's novelty lies in combining SMOTE with BoW and Random Forest to overcome data imbalance. This approach is a significant reference for future sentiment analysis research. It provides practical insights that PT PLN (Persero) can use to improve the quality of its application services.

CLUSTERING TRAFO DISTRIBUSI MENGGUNAKAN ALGORITMA SELF-ORGANIZING MAP Khotimah, Tutik; Syukur, Abdul; Soeleman, M. Arief
Simetris: Jurnal Teknik Mesin, Elektro dan Ilmu Komputer Vol 8, No 1 (2017): JURNAL SIMETRIS VOLUME 8 NO 1 TAHUN 2017
Publisher : Fakultas Teknik Universitas Muria Kudus

Salah satu cara untuk mengetahui beban sebuah trafo distribusi PLN masih memenuhi batas normal atau overload adalah dengan melakukan pengukuran beban trafo tersebut. Pada PLN Area Pelayanan Jaringan Kudus, pengukuran beban dilakukan baik pada siang hari mau pun pada malam hari. Hasil pengukuran tersebut memiliki kemungkinan berbeda. Hal ini disebabkan pada siang hari penggunaan beban cenderung kecil, sedangkan pada malam hari pemakaian beban lebih besar. Hal ini menyebabkan sulitnya menentukan beban trafo tersebut masih normal atau overload. Untuk memetakan beban trafo distribusi secara cepat dan akurat, diperlukan teknik data mining yaitu clustering. Penelitian ini dilakukan dengan menerapkan algoritma Self Organizing Map (SOM). Dengan SOM dihasilkan nilai akurasi sebesar 93% terhadap hasil pengukuran beban trafo distribusi pada siang hari dan sebesar 84% terhadap hasil pengukuran beban trafo distribusi pada malam hari. Sedangkan error yang dihasilkan dari pemetaan dengan SOM sebesar 7% terhadap hasil pengukuran beban trafo distribusi pada siang hari dan sebesar 16% terhadap hasil pengukuran beban trafo distribusi pada malam hari.

Enhancing Monkeypox Skin Lesion Classification With Resnet50v2: The Impact Of Pre-Trained Models From Medical And General Domains Azhar, Saifulloh; Syukur, Abdul; Soeleman, M. Arief; Affandy, Affandy; Marjuni, Aris
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 3 (2025): JUTIF Volume 6, Number 3, Juni 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.3.4486

The monkeypox outbreak has emerged as a pressing global health concern, as evidenced by the rising number of cases reported in various countries. This rare zoonotic disease, caused by the Monkeypox virus (MPXV) of the Poxviridae family, is commonly found in Africa. However, since 2022, cases have also spread to various countries, including Indonesia. The dermatological symptoms exhibited by affected individuals vary, with the potential for further transmission through contamination. Early and accurate detection of monkey pox disease is therefore essential for effective treatment. The present study aims to improve the classification of Monkey Pox using the modified Resnet50V2 model, trained using pre-training datasets namely ImageNet and HAM10000, where batch size and learning rate parameters were adjusted. The study achieved high accuracy in distinguishing monkeypox cases, with 98.43% accuracy for Resnet50V2 with pretrained ImageNet and 70.57% accuracy for Resnet50V2 with pretrained HAM10000. Future research will focus on refining these models, exploring hybrid approaches incorporating convolutional neural networks, this advancement contributes to the development of automated early diagnosis tools for monkeypox skin conditions, especially in resource-limited clinical settings where access to dermatology experts is limited.

Enhancing Vision Transformer Performance with Rotation Based Augmentation for Classifying Images of Colon Cancer Pathology Prasetya, Rudy Eko; Soeleman, M. Arief; Al Zami, Farrikh; Affandy, Affandy; Marjuni, Aris; Assaqty, Mohammad Iqbal Saryuddin
INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Vol 9 No 2 (2025): August 2025
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/intensif.v9i2.24918

Background: In medical imaging, classifying images of colon cancer pathology is still an essential challenge, especially for facilitating early diagnosis and successful intervention. Recently, Vision Transformer (ViT) models have demonstrated great promise for a variety of computer vision tasks, including the classification of medical images. However, the lack of annotated medical datasets and the intrinsic unpredictability of histopathology pictures sometimes restrict their performance. Objective: This study aims to enhance the performance of ViT models in colon cancer pathology classification by introducing a targeted data augmentation strategy, with a particular focus on rotation-based augmentation. Methods: We proposed a data augmentation pipeline that uses controlled changes to improve the number and diversity of training data. Like Rotation, Flip and Geometry are emphasized to replicate the real-world tissue orientation variations that are frequently seen in colon pathology slides. 10,000 JPEG pictures of colon cancer pathology, each with a resolution of 768 x 768 pixels, are used to train the models. We use models trained with and without the suggested augmentation pipeline to compare ViT performance across accuracy, sensitivity, and specificity in order to assess the impact of augmentation. Results: According to study results, rotation-based augmentation enhances ViT performance, achieving up to 99.30% accuracy and 99.50% sensitivity while preserving training times. In real-world pathology settings, where slide orientation varies greatly and can affect categorization consistency, these enhancements are especially pertinent. Conclusion: The proposed rotation-centric data augmentation technique enhances the performance of the ViT model in the classification of images showing colon cancer pathology.

Peningkatan Akurasi Klasifikasi Awan Cumulonimbus Dari Satelit Himawari-8 Saat Cuaca Ekstrem Dengan Menggunakan Metode Grayscale Thermal Image Dan Neural Network Backpropagation Triyotomo, Triyotomo; Wulandari, Sari Ayu; Soeleman, M. Arief; Zami , Farrikh Al
Jurnal Locus Penelitian dan Pengabdian Vol. 4 No. 10 (2025): : JURNAL LOCUS: Penelitian dan Pengabdian
Publisher : Riviera Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58344/locus.v4i10.4915

Cumulonimbus is the only type of cloud that can produce hail, lightning, and thunder. This type of cloud can cause extreme weather that causes damage to public infrastructure and can also cost lives. This research aims to improve Cumulonimbus cloud detection on the Himawari-8 satellite using a combination of the Grayscale Thermal Image method and the method of Artificial Neural Network Backpropagation. The data was taken during the transition season, which is a potential time the onset of extreme weather caused by Cumulonimbus clouds is quite large, and the consequences incurred can cause very significant losses. To detect Cumulonimbus, The Himawari-8 Satellite Image is pre-processed so that an image is obtained gray thermal, then the image is converted into digital data in the form of numbers and from the characterization of the results using histograms. The last process is classified using Artificial Neural Network Propagation. All processes in this study use Matlab to obtain the best classification accuracy. The expected result is an increase in the value of accuracy when using the method of grayscale thermal image compared without using this method. Each accuracy value training data, validating data, and testing data obtained increased from 96.6%, 84.46%, and 80.02 to 100%, 88.9%, and 91.7%.

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