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All Journal TEKNOLOGIA IJCOSIN : Indonesian Journal of Community Service and Innovation Jurnal: International Journal of Engineering and Computer Science Applications (IJECSA) Buffer Informatika Advance Sustainable Science, Engineering and Technology (ASSET)
Henri Tantyoko
Institut Teknologi Telkom Purwokerto
Agriculture, Biological Sciences & Forestry Automotive Engineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mathematics Social Sciences Other

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PERBANDINGAN PEMBOBOTAN UNTUK KLASIFIKASI TOPIK BERITA MENGGUNAKAN DECISION TREE Henri Tantyoko; Adiwijaya Adiwijaya; Untari Novia Wisesty
JURNAL TEKNOLOGIA Vol 2 No 1 (2019): Jurnal Teknologia
Publisher : Aliansi Perguruan Tinggi Badan Usaha Milik Negara (APERTI BUMN)

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Abstract

News is a media to add insight into the outside world, many events that can not be known directly, because it is news that can make it easier to find out more extensive information about the increase. News dissemination consists of online for internet and offline for print media. In the present era, the development of the internet is very fast, making it easier to access information, media delivery of news becomes varied with the internet. Many news available online cause problems because news published by publishers can make mistakes in categorizing news content into the right category. Need technical contributions to categorize news automatically. Categorization of the method used. In this study, the authors used the Decision Tree classification method. A process that is no less important before classification is the word weighting technique. To get optimal accuracy, the authors combine classification techniques using Decision Tree with word weighting techniques TF.ABS, TF.CHI2, TF.RF and TF.IDF. Receive TF.ABS which has the
Implementasi Internet of Things (IoT) pada SMK AL Hikmah 2 dalam mendukung Revolusi Industri 4.0 Aulia Desy Aulia Nur Utomo; Anggi Zafia; Bita Parga Zen; Dimas Fanny Hebrasianto Permadi; Henri Tantyoko; Yoso Adi Setyoko
Indonesian Journal of Community Service and Innovation (IJCOSIN) Vol 3 No 2 (2023): Juli 2023
Publisher : LPPM IT Telkom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/ijcosin.v3i2.1160

Abstract

In order to increase knowledge about technology, especially in the field of information technology, material debriefing on the Internet of Things (IoT) is conducted at Pondok Pesantren Al-Hikmah 2. The purpose of this material debriefing is important for the development of students in their learning material to support the Information Technology learning syllabus . In addition, as an illustration of the implementation of IoT applied in the industry. The delivery method is by delivering basic theory, workshops and implementation. The activity was carried out in the classroom of SMK AL-Hikmah 2 Sirampog, Brebes and was attended by 60 participants. Participants are trained on using microcontrollers as well as sensors and IoT devices. The participants were very enthusiastic about holding this training and workshop, as seen from the results of the participant satisfaction questionnaire which showed that the average answer was very satisfied as many as 31 students and 29 students answered they were satisfied with this training.
Handling Imbalance Data using Hybrid Sampling SMOTE-ENN in Lung Cancer Classification Muhammad Abdul Latief; Luthfi Rakan Nabila; Wildan Miftakhurrahman; Saihun Ma'rufatullah; Henri Tantyoko
International Journal of Engineering and Computer Science Applications (IJECSA) Vol 3 No 1 (2024): March 2024
Publisher : Universitas Bumigora Mataram-Lombok

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/ijecsa.v3i1.3758

Abstract

The classification problem is one instance of a problem that is typically handled or resolved using machine learning. When there is an imbalance in the classes within the data, machine learning models have a tendency to overclassify a greater number of classes. The model will have low accuracy in a few classes and high accuracy in many classes as a result of the issue. The majority of the data has the same number of classes, but if the difference is too great, it will differ. The issue of data imbalance is also evident in the data on lung cancer, where there are 283 positive classes and negative classes 38. Therefore, this research aims to use a hybrid sampling technique, combining Synthetic Minority Over-sampling Technique (SMOTE) with Edited Nearest Neighbors (ENN) and Random Forest, to balance the data of lung cancer patients who experience class imbalance. This research method involves the SMOTE-ENN preprocessing method to balance the data and the Random Forest method is used as a classification method to predict lung cancer by dividing training data and testing 10-fold cross validation. The results of this study show that using SMOTE-ENN with Random Forest has the best performance compared to SMOTE and without oversampling on all metrics used. The conclusion is using the SMOTE-ENN hybrid sampling technique with the Random Forest model significantly improves the model's ability to identify and classify data.
Multi-Horizon Short-Term Residential Load Forecasting Using Decomposition-Based Linear Neural Network Henri Tantyoko; Satriawan Rasyid Purnama; Etna Vianita
Advance Sustainable Science Engineering and Technology Vol. 7 No. 3 (2025): May - July
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v7i3.2033

Abstract

Short-Term Load Forecasting is crucial for grid stability and real-time energy management, particularly in residential settings where consumption is highly volatile and influenced by behavioral and external factors. Traditional models struggle to capture complex, non-linear patterns. This study proposes a forecasting framework based on the DLinear model, which decomposes time series data into trend and seasonal components using a simple linear neural network architecture. Designed for multi-horizon forecasting, the model predicts electricity demand across several future time points simultaneously. Experimental results show that DLinear performs best at a 24-hour prediction length, achieving the lowest MSE of 41.58 and MAE of 5.11, indicating improved accuracy with longer horizons. These results confirm DLinear’s robustness and efficiency in modeling dynamic residential electricity consumption patterns.
Klasifikasi Produktivitas Pekerja Garmen Menggunakan Algoritma Random Forest: Classification of Garment Worker Productivity Using Random Forest Algorithm Luthfi Rakan Nabila; Fiqki Haidar Amrulloh; Ghilman Farhani Putra Aji; Rio Ghaniy Septiansyah; Vincentius Sagi Alban Anindyajati; Henri Tantyoko
Buffer Informatika Vol. 10 No. 1 (2024): Buffer Informatika
Publisher : Department of Informatics Engineering, Faculty of Computer Science, University of Kuningan, Indonesia

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Abstract

Penelitian ini membahas mengenai penerapan algoritma machine learning dalam melakukan klasifikasi produktivitas pekerja garmen. Penelitian ini menggunakan dataset produktivitas garmen yang didapatkan dari situs UC Irvine Machine Learning Repository dengan rentang waktu dari tanggal 1 januari 2015 sampai 11 maret 2015 dengan total data sebanyak 1197 baris. Algoritma yang diterapkan pada penelitian ini adalah random forest dengan hyperparameter tuning untuk melakukan klasifikasi produktivitas pekerja garmen. Metodologi penelitian ini melibatkan pengolahan data seperti pemilihan fitur yang relevan, transformasi data, dan normalisasi guna mendapatkan hasil evaluasi terbaik. pada penelitian ini juga dilakukan percobaan dengan decision tree dan algoritma svm sebagai pembandingnya. Algoritma random forest mengungguli algoritma lain dengan akurasi sebesar 94.36% di mana akurasi tersebut sudah cukup bagus dalam untuk mengklasifikasi produktivitas
Utilizing Sequential Pattern Mining and Complex Network Analysis for Enhanced Earthquake Prediction Henri Tantyoko; Nurjanah, Dade; Rusmawati, Yanti
Advance Sustainable Science Engineering and Technology Vol. 6 No. 4 (2024): August-October
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v6i4.1003

Abstract

Earthquakes are natural events caused by the movement of the earth's plates, often triggered by the energy release from hot liquid magma. Predicting earthquakes is crucial for raising public awareness and preparedness in seismically active areas. This study aims to predict earthquake activity by identifying patterns in seismic events using Sequential Pattern Mining (SPM). To enhance the prediction accuracy, Sequential Rule Mining (SRM) is applied to derive rules with confidence values from these patterns. The results show that using betweenness centrality as a weight increases the prediction accuracy to 83.940%, compared to 78.625% without weights. Using eigenvector centrality as a weight yields an accuracy of 83.605%. These findings highlight the potential of using centrality measures to improve earthquake prediction systems, offering valuable insights for disaster preparedness and risk mitigation.
Co-Authors Adiwijaya Anggi Zafia Aulia Desy Nur Utomo Bita Parga Zen Dade Nurjanah Dimas Fanny Hebrasianto Permadi Etna Vianita Fiqki Haidar Amrulloh Ghilman Farhani Putra Aji Luthfi Rakan Nabila Luthfi Rakan Nabila Muhammad Abdul Latief Rio Ghaniy Septiansyah Rusmawati, Yanti Saihun Ma'rufatullah Satriawan Rasyid Purnama Untari Novia Wisesty Vincentius Sagi Alban Anindyajati Wildan Miftakhurrahman Yoso Adi Setyoko