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All Journal TEKNIK INFORMATIKA Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Edukasi dan Penelitian Informatika (JEPIN) Proceeding SENDI_U MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer JURIKOM (Jurnal Riset Komputer) JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) TIN: TERAPAN INFORMATIKA NUSANTARA
Muhammad Ibnu Choldun Rachmatullah
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PENGKLASTERAN TERHADAP NEGARA-NEGARA DENGAN JUMLAH KASUS COVID 19 TERBESAR Rachmatullah, Muhammad Ibnu Choldun
Proceeding SENDI_U 2021: SEMINAR NASIONAL MULTI DISIPLIN ILMU DAN CALL FOR PAPERS
Publisher : Proceeding SENDI_U

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Pandemi Covid 19 telah menyebar di seluruh negara di dunia. Berdasarkan data pada tanggal 11 Juli 2021 telah menyebabkan 187.393.771 orang terinfeksi di seluruh dunia. Paper ini bertujuan untuk mengelompokkan negara dengan jumlah kasus terbesar berdasar karakteristik kesamaan dan perbedaan dengan mempertimbangkan atribut jumlah kasus per 1 juta penduduk, jumlah kematian per 1 juta penduduk, dan jumlah tes per 1 juta penduduk. Metode pengklasteran yang digunakan adalah K-Means, sedangkan penentuan jumlah klaster optimal dengan menerapkan kriteria Elbow. Dari penerapan kriteria Elbow ini dari hasil eksperimen diperoleh jumlah klaster optimal=5, sehingga akan membagi 30 negara dengan jumlah kasus terbesar ke dalam lima kelompok/kategori. Dari pengelompokan ini dapat diketahui karakteristik 30 negara dengan jumlah kasus Covid 19 terbesar, berdasar jumlah kasus dan jumlah pengetesan per 1 juta penduduk.
Proposed Modification of K-Means Clustering Algorithm with Distance Calculation Based on Correlation Muhammad Ibnu Choldun Rachmatullah
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol 8, No 1 (2022): March
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v8i1.23696

Abstract

Clustering is a technique in data mining that groups a set of data into groups (clusters) of similar data. In general, there are two methods of clustering, namely the hierarchical method and the partition method. One of the most commonly used partition clustering methods in clustering is K-Means. The use of K-means method has been widely used in various fields with various purposes. Many research has been carried out to improve the performance of the K-Means method, for example, by modifying the method of determining the initial centroid or determining the appropriate number of clusters. In this research, the modification of the K-Means algorithm was carried out in calculating the distance by considering the correlation value between attributes. Attributes that have a high correlation value are assumed to have similar characteristics so that they determine the location of data in a particular cluster. The steps of the proposed method are: calculating the correlation value between attributes, determining the cluster centroid, calculating the distance by considering the value of correlation, and determining the data into certain clusters. The first contribution of this research is to propose a new distance calculation technique in the K-Means algorithm by considering correlation and the second contribution is to apply the proposed algorithm to a specific dataset, namely Iris dataset. In this research, the performance calculation of the modified algorithm was also carried out. From the experimental results using the Iris dataset, the proposed modification of the K-Means algorithm has fewer iterations than the original K-Means method, so that it requires less processing time. The original K-Means method requires 8 iterations, while the proposed method requires only 6 iterations. The proposed method also produces a higher accuracy rate of 89.33% than the original K-Means method, which is 82.67%.
Perbandingan Metoda K-NN, Random Forest dan 1D CNN untuk Mengklasifikasi Data EEG Eye State Muhammad Ibnu Choldun Rachmatullah; Aryaputra Wicaksono; Virdiandry Putratama
Journal of Information System Research (JOSH) Vol 4 No 2 (2023): January 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v4i2.2998

Abstract

The use of machine learning / machine learning methods is very important in developing identification of the status of the human eye, especially in terms of processing Electroencephalogram (EEG) signals to identify eye status. In previous research the method used can be a combination method between supervised learning and unsupervised learning, or a single method using supervised learning. In this study, the EEG Eye State classification uses a single method with supervised learning, namely using the following methods: K-nearest neghbors (k-NN), random forest, and 1D Convolutional Neural Networks (1D CNNs). The performance of the three classifier methods is measured using four measures, namely: accuracy, recall, precision, and F1-Score. From the experimental results it was found that the k-NN method has the best performance compared to the other two methods in terms of the four measures used, where the value of each measure is: accuracy = 82.30%; recall=82.30%; precision= 82.36%; and F1-Score=82.30%. K-NN is more suitable for classifying EEG Eye State than the other two methods, because all input attributes are from the dataset. has a data type of real numbers.
Penerapan SMOTE untuk Meningkatan Kinerja Klasifikasi Penilaian Kredit Muhammad Ibnu Choldun Rachmatullah
JURIKOM (Jurnal Riset Komputer) Vol 10, No 1 (2023): Februari 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v10i1.5612

Abstract

Machine learning techniques are widely used in various fields and data is needed to train models. However, the distribution of classes in most real-world datasets turns out to be not always balanced, and can be very imbalanced. If the data is imbalanced, the performance of the classifier is highly dependent on the majority class, causing problems in determining performance. One technique that can be applied to balance the data is the Synthetic Minority Oversampling Technique (SMOTE). SMOTE is applied to credit scoring using the German Credit Data (GCD) dataset, and then classified using four classification methods, namely: random forest, K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Multilayer Perceptron (MLP). The performance measure of implementing SMOTE in each classifier method is measured using: recall, precision, F1-Score, and AUC. Accuracy values are also measured to see if the accuracy is suitable for calculating performance on imbalanced datasets. Based on performance measures: recall, precision, F1-Score, and AUC, then applying SMOTE to the dataset and then classifying it using four methods shows an increase in performance. The highest performance measure: recall = 82.00% with the random forest method, precision = 75.35 with the MLP method, F1-Score = 76.93% with the MLP method, and AUC = 0.832 with the random forest method. The accuracy value after SMOTE slightly decreased in the random forest, KNN, and SVM methods, while with MLP the accuracy value increased slightly. The contribution of this research is to show the need for imbalanced data handling to improve the performance of classifier algorithms, especially for credit rating datasets.
Pemodelan Perkembangan New Cases Covid-19 di Indonesia Menggunakan Multi-Layer Perceptron dan Support Vector Machine Muhammad Ibnu Choldun Rachmatullah
JEPIN (Jurnal Edukasi dan Penelitian Informatika) Vol 8, No 2 (2022): Volume 8 No 2
Publisher : Program Studi Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/jp.v8i2.53919

Abstract

Meningkatnya ketersediaan data historis dalam jumlah besar dan kebutuhan untuk membuat perkiraan yang akurat tentang perilaku masa depan menjadi perhatian khusus dalam mencari teknik yang dapat menarik kesimpulan dari mengamati hubungan antara data tertentu, antara data masa lalu dan data masa depan. Domain peramalan mengalami peningkatan sejak tahun 1960-an, dengan metode statistik linier, misalnya menggunakan model ARIMA. Baru-baru ini, model pembelajaran mesin telah menarik perhatian dan dapat digunakan sebagai teknik lain selain model statistik klasik untuk kasus peramalan. Penelitian ini memprediksi perubahan kasus baru positif Covid-19 per satu juta penduduk (new cases per million Covid-19) di Indonesia menggunakan pembelajaran mesin. Pemodelan perubahan new cases per million diperlukan karena penyakit ini merupakan penyakit baru, sehingga sampai saat ini belum ada pemodelan deret waktu yang cukup akurat untuk menggambarkan kasus tersebut. Teknik machine learning yang akan digunakan adalah Multi-Layer Perceptron (MLP) dan Support Vector Machine (SVM) dan dibandingkan kinerja dari kedua teknik tersebut. Dari hasil perhitungan kinerja, prediksi new cases per million Covid-19 yang dilakukan dengan menggunakan SVM(RMSE = 9,053) memiliki kinerja yang lebih baik dibandingkan dengan menggunakan MLP (RMSE = 10,284). Nilai RMSE yang lebih kecil menunjukkan kinerja yang lebih baik.
Forecasting Data Time Series Menggunakan MLP dan LSTM untuk Memprediksi Jumlah Produksi Bir Rachmatullah, Muhammad Ibnu Choldun
TIN: Terapan Informatika Nusantara Vol 6 No 4 (2025): September 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i4.7755

Abstract

Time series data forecasting is an important approach in various sectors such as finance, energy, and healthcare. As technology advances, deep learning methods such as Multi-Layer Perceptron (MLP) and Long Short-Term Memory (LSTM) are increasingly being used to improve prediction accuracy. This study compares the performance of these two methods in forecasting a time series dataset of monthly beer production in Australia. The model was trained and tested using a 70% training and 30% testing data split. Performance evaluation was based on the Root Mean Square Error (RMSE) value after 10 experimental repetitions. The results show that MLP has a lower RMSE value and a smaller standard deviation than LSTM, both on the training and testing data. This indicates that MLP is more stable and efficient in handling datasets with simple patterns and low complexity, while LSTM tends to require more intensive tuning and has a higher risk of overfitting. Therefore, MLP is recommended as a lighter and more consistent alternative forecasting method for similar data scenarios.
Sentiment Analysis of Pospay Application Reviews Using the Bert Deep Learning Method Mulyati, Erna; Muhammad Ibnu Choldun Rachmatullah; Adri Sapta Firmansyah
JURNAL TEKNIK INFORMATIKA Vol. 18 No. 2: JURNAL TEKNIK INFORMATIKA
Publisher : Department of Informatics, Universitas Islam Negeri Syarif Hidayatullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/jti.v18i2.41116

Abstract

E-money usage in Indonesia has grown significantly due to increasing internet penetration and smartphone adoption. Digital transactions are becoming more common, with platforms like GoPay, OVO, and Dana leading the market. The government and financial institutions actively support this shift through regulations and initiatives. This study analyzes user sentiment on the Pospay application using the BERT deep learning method, based on 16,760 Google Play Store reviews. To the best of our knowledge, this is the first study to apply BERT for sentiment analysis of Pospay user reviews in Indonesia. The goal is to understand user perceptions and satisfaction. BERT helps capture subtle nuances in reviews, including informal expressions and abbreviations like "gk" for negative sentiment. The model achieves high accuracy, with precision scores of 0.82 (negative) and 0.93 (positive), and recall scores of 0.92 (negative) and 0.93 (positive). Findings suggest PT Pos should enhance application stability, security, transaction processing, and customer service. Regular updates are recommended to improve performance and user satisfaction.
The Application of Repeated SMOTE for Multi Class Classification on Imbalanced Data Muhammad Ibnu Choldun Rachmatullah
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 22 No. 1 (2022)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v22i1.1803

Abstract

One of the problems that are often faced by classifier algorithms is related to the problem of imbalanced data. One of the recommended improvement methods at the data level is to balance the number of data in different classes by enlarging the sample to the minority class (oversampling), one of which is called The Synthetic Minority Oversampling Technique (SMOTE). SMOTE is commonly used to balance data consisting of two classes. In this research, SMOTE was used to balance multi-class data. The purpose of this research is to balance multi-class data by applying SMOTE repeatedly. This iterative process needs to be applied if the number of unbalanced data classes is more than two classes, because the one-time SMOTE process is only suitable for binary classification or the number of unbalanced data classes is only one class. To see the performance of iterative SMOTE, the SMOTE datasets were classified using a neural network, k-NN, Nave Bayes, and Random Forest and the performance measures were measured in terms of accuracy, sensitivity, and specificity. The experiment in this research used the Glass Identification dataset which had six classes, and the SMOTE process was repeated five times. The best performance was achieved by the Random Forest classifier method with accuracy = 86.27%, sensitivity = 86.18%, and specificity = 95.82%. The result of experiment present that repeated SMOTE results can increase the performance of classification.
Co-Authors Adri Sapta Firmansyah Aryaputra Wicaksono Erna Mulyati Virdiandry Putratama