Articles
<![CDATA[PERBANDINGAN PEMBOBOTAN UNTUK KLASIFIKASI TOPIK BERITA MENGGUNAKAN DECISION TREE ]]>
<![CDATA[Henri Tantyoko]]>;
<![CDATA[Adiwijaya Adiwijaya]]>;
<![CDATA[Untari Novia Wisesty]]>
<![CDATA[ JURNAL TEKNOLOGIA Vol 2 No 1 (2019): Jurnal Teknologia ]]>
Publisher : <![CDATA[Aliansi Perguruan Tinggi Badan Usaha Milik Negara (APERTI BUMN) ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
Full PDF (8311.757 KB)
<![CDATA[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]]>
<![CDATA[Implementasi Internet of Things (IoT) pada SMK AL Hikmah 2 dalam mendukung Revolusi Industri 4.0 ]]>
<![CDATA[Aulia Desy Aulia Nur Utomo]]>;
<![CDATA[Anggi Zafia]]>;
<![CDATA[Bita Parga Zen]]>;
<![CDATA[Dimas Fanny Hebrasianto Permadi]]>;
<![CDATA[Henri Tantyoko]]>;
<![CDATA[Yoso Adi Setyoko]]>
<![CDATA[ Indonesian Journal of Community Service and Innovation (IJCOSIN) Vol 3 No 2 (2023): Juli 2023 ]]>
Publisher : <![CDATA[LPPM IT Telkom Purwokerto ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.20895/ijcosin.v3i2.1160
<![CDATA[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.]]>
<![CDATA[Handling Imbalance Data using Hybrid Sampling SMOTE-ENN in Lung Cancer Classification ]]>
<![CDATA[Muhammad Abdul Latief]]>;
<![CDATA[Luthfi Rakan Nabila]]>;
<![CDATA[Wildan Miftakhurrahman]]>;
<![CDATA[Saihun Ma'rufatullah]]>;
<![CDATA[Henri Tantyoko]]>
<![CDATA[ International Journal of Engineering and Computer Science Applications (IJECSA) Vol 3 No 1 (2024): March 2024 ]]>
Publisher : <![CDATA[Universitas Bumigora Mataram-Lombok ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.30812/ijecsa.v3i1.3758
<![CDATA[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.]]>
<![CDATA[PREDIKSI POTENSIAL GEMPA BUMI INDONESIA MENGGUNAKAN METODE RANDOM FOREST DAN FEATURE SELECTION ]]>
<![CDATA[Tantyoko, Henri]]>;
<![CDATA[Sari, Dian Kartika]]>;
<![CDATA[Wijaya, Andreas Rony]]>
<![CDATA[ IDEALIS : InDonEsiA journaL Information System Vol 6 No 2 (2023): Jurnal IDEALIS Juli 2023 ]]>
Publisher : <![CDATA[Universitas Budi Luhur ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.36080/idealis.v6i2.3036
<![CDATA[Gempa bumi adalah suatu peristiwa alamiah yang terjadi saat terjadi pelepasan energi secara tiba-tiba dalam kerak bumi, mengakibatkan getaran dan guncangan pada permukaan bumi. Gempa bumi merupakan salah satu bencana alam yang dapat menyebabkan kerusakan fisik yang besar, dampak ekonomi yang signifikan, dan hilangnya nyawa manusia. Beberapa penyebab gempa bumi antara lain aktivitas tektonik lempeng bumi, pergerakan lempeng tektonik, dan deformasi kerak bumi. Untuk mengurangi jumlah korban jiwa, perlu dilakukan prediksi kapan gempa bumi akan terjadi di suatu wilayah. Salah satu cara untuk memprediksi ialah dengan menggunakan metode Machine Learning yaitu Random Forest (RF), metode ini memanfaatkan beberapa pohon keputusan yang selanjutnya dilakukan voting untuk menentukan keputusan akhir prediksi . Model yang baik adalah model yang menghasilkan kesalahan seminimal mungkin. Oleh karena itu, penulis melakukan skema seleksi fitur untuk mengolah fitur-fitur yang memiliki korelasi yang kuat. Prediksi menggunakan RF dengan seleksi fitur menghasilkan F1 score sebesar 92.23%, yang lebih baik 5.02% dibandingkan tanpa menggunakan seleksi fitur. Metode RF + Seleksi Fitur ini juga jauh lebih baik jika dibandingkan metode machine learning tradisional lainnya seperti SVM, Naïve Bayes, dan Decision Tree.]]>
<![CDATA[Comparison of the Word2vec Skipgram Model Method Linkaja Application Review using Bidirectional LSTM Algorithm and Support Vector Machine ]]>
<![CDATA[Ayuningtyas, Puji]]>;
<![CDATA[Tantyoko, Henri]]>
<![CDATA[ JUSTIN (Jurnal Sistem dan Teknologi Informasi) Vol 12, No 1 (2024) ]]>
Publisher : <![CDATA[Jurusan Informatika Universitas Tanjungpura ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.26418/justin.v12i1.72530
<![CDATA[Word embedding is a phase in word processing that seeks to convert each word into a vector representation. Word2Vec is a sort of word embedding that is frequently utilized in natural language processing research. Choosing the proper algorithm can help increase the performance of the word embedding method while doing text data categorization tasks. This research uses the Bidirectional LSTM deep learning algorithm and the Support Vector Machine (SVM) machine learning algorithm. The crawling approach was used to obtain data by accessing the LinkAja Application ID on the Google Play Store. The total number of rows in the dataset was 35560. Labeling data involves categorizing it into two target classes: positive (score 1) and negative (score 0). This study employs the Word2Vec approach with skipgram architecture during the vectorization stage. Vector size, window, min count, and sg are the four parameters employed. The bidirectional LSTM architecture employs a sequential model that consists of three neural network layers: embedding, bidirectional, and dense. In the meanwhile, the SVM architecture employs the Radial Basis Function (RBF) kernel parameters. For the final stage of algorithm testing, the accuracy of the bidirectional LSTM (BiLSTM) algorithm was 0.9505, which means it was higher than the support vector machine (SVM) algorithm with an accuracy value of 0.93.]]>
<![CDATA[Utilizing Sequential Pattern Mining and Complex Network Analysis for Enhanced Earthquake Prediction ]]>
<![CDATA[Henri Tantyoko]]>;
<![CDATA[Dade Nurjanah]]>;
<![CDATA[Yanti Rusmawati]]>
<![CDATA[ Advance Sustainable Science Engineering and Technology Vol. 6 No. 4 (2024): August-October ]]>
Publisher : <![CDATA[Science and Technology Research Centre Universitas PGRI Semarang ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.26877/asset.v6i4.1003
<![CDATA[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.]]>
<![CDATA[Handling Imbalance Data using Hybrid Sampling SMOTE-ENN in Lung Cancer Classification ]]>
<![CDATA[Latief, Muhammad Abdul]]>;
<![CDATA[Nabila, Luthfi Rakan]]>;
<![CDATA[Miftakhurrahman, Wildan]]>;
<![CDATA[Ma'rufatullah, Saihun]]>;
<![CDATA[Tantyoko, Henri]]>
<![CDATA[ International Journal of Engineering and Computer Science Applications (IJECSA) Vol. 3 No. 1 (2024): March 2024 ]]>
Publisher : <![CDATA[Universitas Bumigora Mataram-Lombok ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.30812/ijecsa.v3i1.3758
<![CDATA[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.]]>
<![CDATA[Hybrid Algoritma Vgg16-Net Dengan Support Vector Machine Untuk Klasifikasi Jenis Buah dan sayuran ]]>
<![CDATA[Aditya Dwi Putro]]>;
<![CDATA[Henri Tantyoko]]>
<![CDATA[ Jurnal Teknologi Informasi dan Multimedia Vol. 5 No. 2 (2023): August ]]>
Publisher : <![CDATA[Sekawan Institut ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.35746/jtim.v5i2.335
<![CDATA[Arsitektur deep learning VGG16 terbukti efektif dalam hal melakukan klasifikasi citra pada dataset ImageNet, akan tetapi memiliki keterbatasan dalam jumlah parameter sangat banyak dan potensi overfitting pada dataset kecil. SVM memiliki kelebihan dalam hal menangani masalah overfitting pada dataset yang relatif kecil, sementara VGG16 memiliki keunggulan dalam mengekstraksi fitur yang berkualitas dari citra dengan performa yang sangat baik. SVM juga dapat membantu memperbaiki kinerja klasifikasi pada VGG16 dengan meminimalkan risiko overfitting dan meningkatkan akurasi klasifikasi pada dataset yang relatif kecil. Oleh karena itu, penulis memilih untuk hybrid algoritma VGG16Net Dengan Support Vector Machine Untuk Klasifikasi Jenis buah dan sayuran, yang nantinya arsitektur VGG16 digunakan untuk ekstraksi fitur dari citra dan fitur-fitur tersebut dijadikan input untuk SVM. Keputusan menggunakan VGG16 digabungkan dengan SVM adalah untuk meningkatkan akurasi klasifikasi dataset citra buah dan sayuran, Namun, penggunaan SVM membutuhkan pemilihan parameter yang tepat dan teknik prapemrosesan data yang tepat untuk mencapai hasil yang baik. Dan dalam penelitian ini penulis berhasil mengklasifikasikan citra buah dan sayuran, akurasi sebelum hybrid svm mendapatkan 94.52% training accuracy dan testing (validation) accuracy sebesar 87.85%. dan hasil loss mendapat training loss sebesar 0.58 dan testing loss accuracy sebesar 12.5%. Setelah dilakukan hybrid vgg16 dengan svm didapatkan training accuracy sebesar 99.87 % dan testing (validation) accuracy sebesar 91.76 %. Untuk hasil loss mendapat training loss sebesar 0.13 dan testing loss accuracy sebesar 8.24%. Oleh karena itu, arsitektur CNN VGG-16Net digabungkan dengan SVM dapat menghasilkan model klasifikasi yang baik, terutama pada dataset yang relatif kecil dan dapat menjadi pilihan yang sesuai dalam klasifikasi citra.]]>
<![CDATA[An Efficient Bidirectional Gated Recurrent Unit Approach for Student Study Duration Modeling and Timely Graduation Forecasting ]]>
<![CDATA[Satriawan Rasyid Purnama]]>;
<![CDATA[Henri Tantyoko]]>;
<![CDATA[Etna Vianita]]>
<![CDATA[ Jurnal Masyarakat Informatika Vol 16, No 2 (2025): November 2025 ]]>
Publisher : <![CDATA[Department of Informatics, Universitas Diponegoro ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.14710/jmasif.16.2.73275
<![CDATA[Delays in student graduation remain a persistent challenge in higher education, with approximately 28% of students requiring more than four years to complete their studies, exceeding the standard duration. This study addresses the issue by proposing a predictive model to estimate students’ graduation year using a Bidirectional Gated Recurrent Unit (BiGRU) neural network. The model is trained on a combination of academic and financial indicators, including Grade Point (GP) scores from the first to the fifth semester, cumulative Grade Point Average (GPA), and the single tuition fee tier (UKT). The integration of these features allows the model to learn temporal patterns in students’ academic progression and financial capacity. Empirical analysis reveals that students in the UKT 8 group consistently demonstrate superior academic performance, as evidenced by their higher average GPA across semesters, compared to students in lower UKT groups. The BiGRU model achieves a Mean Absolute Percentage Error (MAPE) of 9.5%, indicating high predictive accuracy. These findings highlight the potential of deep learning models, particularly BiGRU, in forecasting academic outcomes. Furthermore, the insights generated from this model can serve as a valuable tool for universities in formulating targeted academic interventions and policies aimed at promoting timely graduation and reducing dropout rates.]]>
<![CDATA[Classification of Real and Fake Images Using Error Level Analysis Technique and MobileNetV2 Architecture ]]>
<![CDATA[Muhamad Nur Baihaqi]]>;
<![CDATA[Aris Sugiharto]]>;
<![CDATA[Henri Tantyoko]]>
<![CDATA[ Jurnal Masyarakat Informatika Vol 16, No 1 (2025): May 2025 ]]>
Publisher : <![CDATA[Department of Informatics, Universitas Diponegoro ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.14710/jmasif.16.1.73283
<![CDATA[Advancements in technology have made image forgery increasingly difficult to detect, raising the risk of misinformation on social media. To address this issue, Error Level Analysis (ELA) feature extraction can be utilized to detect error level variations in lossy-formatted images such as JPEG. This study evaluates the contribution of ELA features in classifying authentic and forged images using the MobileNetV2 model. Two scenarios were tested using the CASIA 2.0 dataset: without ELA and with ELA. Fine-tuning was performed to adapt the model to the new problem. Experimental results show that incorporating ELA improves model accuracy up to 93.1%, compared to only 76.41% in the scenario without ELA. Validation using k-fold cross-validation yielded a high average f1-score of 96.83%, confirming the effectiveness of ELA in enhancing the classification performance of authentic and forged images.]]>
