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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) International Journal of Advances in Intelligent Informatics Indonesian Journal on Computing (Indo-JC) Jurnal Kimia Riset JOIN (Jurnal Online Informatika) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Jiko (Jurnal Informatika dan komputer) Jurnal Teknik Informatika (JUTIF) Charity : Jurnal Pengabdian Masyarakat eProceedings of Engineering Jurnal INFOTEL LOGIC: Jurnal Penelitian Informatika
Isman Kurniawan
Universitas Telkom
Arts Humanities Chemistry Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Social Sciences

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Analisis Teks Pelamar Untuk Klasifikasi Kepribadian Menggunakan Multinomial Naïve Bayes dan Decision Tree Nanda Yonda Hutama; Kemas Muslim Lhaksmana; Isman Kurniawan
JURNAL INFOTEL Vol 12 No 3 (2020): August 2020
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v12i3.505

Abstract

Employees' qualities affect companies' performances and with a large number of applicants, it's difficult to find suitable applicants. To help with it, companies carry out psychological tests to know applicants' personalities, since personality's considered to have a relationship with work performances. But psychological testing requires a lot of effort, cost, and human resources. Thus with a system that can classify personalities through text can help reduce the effort needed. Similar studies carried out with the big five personalities as the theoretical basis and used one of the personality traits, namely using the k-NN method with 65% accuracy. Based on these studies, accuracy can improve by finding the best parameters using all of the big five personalities. This research is conducted based on the big five personality traits and related traits, namely consciousness and agreeableness. The data used is text data that's been labelled, pre-processed and feature selected. The clean text data is used to create a classification model using multinomial Naive Bayes and decision trees. There are 6 models built based on 3 work cultures, decision tree with an accuracy of 33%, 66%, 80%, and multinomial naïve Bayes with an accuracy of 83%, 50%, 60%, which resulted as better performance.
Content-based Filtering Movie Recommender System Using Semantic Approach with Recurrent Neural Network Classification and SGD Salsabil, Adinda Arwa; Setiawan, Erwin Budi; Kurniawan, Isman
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 9, No. 2, May 2024
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v9i2.1940

Abstract

The application of recommendation systems has been applied in various types of platforms, especially applications for watching movies such as Netflix and Disney+. The recommendation system is purposed to make it easier for users, especially in choosing a movie because currently the number of movie productions is increasing every day. This research proposed a CBF movie recommendation system by comparing the performance of several semantic methods to be able to get the best rating prediction results. In order to improve the performance quality to get the best rating prediction results, this research  utilized semantic feature methods by comparing the performance of the evaluation results produced by the TF-IDF method and word embedding applications, such as BERT, GPT-2, RoBERTa, and implemented RNN model to classify the results of rating prediction. The data were used to generate the recommendation system by involving 854 data movie and 39 accounts with a total of 34,056 movie reviews on Twitter. This research has succeeded in getting a method that produced rating predictions, namely RoBERTa. In the classification process with the RNN model and SGD optimization, the measurement results with confusion matrix by classifying the RoBERTA rating prediction obtained an evaluation value of 0.6514 loss, 95.59% accuracy, and 0.6514 precision.
Movie Recommender System Using Cascade Hybrid Filtering with Convolutional Neural Network Arsytania, Ihsani Hawa; Setiawan, Erwin Budi; Kurniawan, Isman
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 10 No. 2 (2024): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v10i2.28146

Abstract

The current technological advancements have made it easier to watch movies, especially through online streaming platforms such as Netflix. Social media platforms like Twitter are used to discuss, share information, and recommend movies to other users through tweets. The user tweets from Twitter are utilized as a film review dataset. Film ratings can be used to build a recommendation system, incorporating Collaborative Filtering (CF) and Content-based Filtering (CBF). However, both methods have their limitations. Therefore, a hybrid filtering approach is required to overcome this problem. The filtering approach involves CF and CBF processes to improve the accuracy of film recommendations. No current research employs the Cascade Hybrid Filtering method, particularly within the context of movie recommendation systems. This study addresses this gap by implementing the Cascade Hybrid Filtering method, utilizing the Convolutional Neural Network (CNN) as the evaluative instrument. This research presents a significant contribution by implementing the Cascade Hybrid Filtering method based on CNN. This research uses several scenarios to compare methods to produce the most accurate model. This study's findings demonstrate that the application of Cascade Hybrid Filtering, incorporating CNN and optimized with RMSProp, yields a movie recommendation system with notable performance metrics, including an MAE of 0.8643, RMSE of 0.6325, and the highest accuracy rate recorded at 86.95%. The RMSprop optimizer, facilitating a learning rate of 6.250551925273976e-06, enhances accuracy to 88.40%, showcasing a remarkable improvement of 6.00% from the baseline. These outcomes underscore the significant contribution of the paper in enhancing the precision and effectiveness of movie recommendation systems.
Enhancing drug-target affinity prediction through pre-trained language model and gated multi-head attention Khoerunnisa, Ghina; Kurniawan, Isman
International Journal of Advances in Intelligent Informatics Vol 11, No 2 (2025): May 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v11i2.1910

Abstract

Drug development requires accurate drug-target interaction (DTI) information to evaluate a drug's potential. However, existing current methods for estimating DTI are slow and expensive. Deep learning offers an efficient and effective alternative by leveraging sequence data for prediction. Nevertheless, the DTI binary classification approach suffers from a large number of non-interacting pairs, resulting in data imbalance and has a negative impact on performance. To address this issue, DTI is modeled as a regression problem known as drug-target affinity (DTA), which predicts the strength of interactions. While various deep learning methods show competitive results in DTA prediction, they face a challenge in capturing specific drug-target patterns with limited data. To overcome the problem, this study leverages pre-trained language models for enhanced representation. Also, we utilize gated multi-head attention (GMHA), which modifies multi-head attention by including dynamic scaling and a gate process to capture the mutual interactions better. The results show that our proposed method exceeds the benchmark and baseline in all evaluation metrics, with concordance index (CI) of 0.893 and 0.872, and modified r-squared (rm2) of 0.673 and 0.723 in Davis and KIBA. Our findings further suggest that pre-trained language models for drug and target receptor representation improve DTA prediction model performance. Also, the GMHA method generally outperforms the simple concatenation method, with more obvious advantages in more complex datasets like KIBA. Our approach provides a competitive enhancement in DTA prediction, suggesting a promising direction for further enhancing drug discovery and development processes.
Detecting Alzheimer's Based on MRI Medical Images by Using External Attention Transformer Ardannur Deswanto, Farrel; Kurniawan, Isman
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 2 (2025): April 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i2.6257

Abstract

Alzheimer's disease is one of the major challenges in medical care this century, affecting millions of people worldwide. Alzheimer's damages neurons and connections in brain areas responsible for memory, language, reasoning, and social behavior. Early detection of this disease enables more effective treatment and proper care planning. Unfortunately, the traditional method of detecting Alzheimer's has several limitations, such as subjective analysis and delayed diagnosis. One commonly used method is visual inspection, which uses magnetic resonance imaging (MRI). The limitations of visual inspection include subjectivity and its time-consuming nature, especially with large or complex MRI datasets, making accurate interpretation a significant challenge. Therefore, an alternative for detecting Alzheimer’s disease is to use deep learning-based MRI image analysis. One promising approach is to implement the External Attention Transformer (EAT) model. It enhances image classification by using two shared external memories and an attention mechanism that filters out redundant information for improved performance and efficiency. The aim of this research is to evaluate and compare the performance of the baseline Convolutional Neural Network (CNN) model, the Vision Transformer (ViT) model, and the EAT model in detecting Alzheimer's using a dataset of 6400 brain MRI images. The EAT model outperforms the baseline CNN model and ViT model in detecting Alzheimer's, achieving its best results with an accuracy of 0.965 and an F1-score of 0.747 for the test data. Our results could be integrated with clinical analysis to assist in the faster diagnosis of Alzheimer's.
Implementasi Metode Gravitational Search Algorithm- Adaboost Untuk Pada Prediksi Diabetes Pada Anak Berdasarkan Data Ekspresi Gen Farid, Mochammad Rafi; Kurniawan, Isman
eProceedings of Engineering Vol. 12 No. 3 (2025): Juni 2025
Publisher : eProceedings of Engineering

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

Abstract

Diabetes merupakan penyakit parah yang terjadipada saat insulin tidak dapat dihasilkan dengan baik plehpankreas atau pada saat tubuh tidak bisa menggunakaninsulin yang diproduksi oleh pankreas secara efektif, Insulinmerupakan suatu hormon dengan fungsi mengontrolglukosa dalam darah. Diabetes melitus tipe 1 (DMT1) seringterjadi pada anak dan remaja hingga 90%. Data yangberisi profil ekspresi gen pada anak-anak dengan T1D danT2D, pengukuran dilakukan saat diagnosis pada awal dandiulang 4 bulan setelahnya , dan juga setelah mendapatkanpengobatan, maka Matriks ekspresi gen kemudianditransposisikan dan tiga fitur demografis yang dianggappenting yaitu usia, jenis kelamin, dan ras. Setelahmelakukan proses GSA, dataset akan dilakukan prosesklasifikasi, dengan menggunakan metode utama yaituAdaptive Boosting (AdaBoost), selanjutnya ditambahkan 2metode ensemble sebagai pembanding yaitu KNeighbors(KNN), Multi- Layer Perceptron (MLP). Kemudiandilakukan hyperparameter tuning bertujuan untuk mencariniai yang paling optimal dengan meningkatkan kinerja padamodel. Parameter scanning pada proses tuning dilakukandengan menggunakan search cross validation (grid searchCV). tersebut akan menjadi tolok ukuruntuk mengevaluaisiketiga model yang digunakan sehingga diperoleh hasilpaling optimal yakni AdaBoost dengan accuracy 0,666 danF1-Score 0,769. Kata kunci: Diabetes melitus, Gravitational Search Algorithm, Multi-Layer Perceptron, Adaptive Boosting, KNeighbors
Peningkatan Keterampilan Teknis dan Pemasaran Bagi Para Pengrajin dan Pengusaha Produk Kulit Sukaregang Garut Kurniawan, Isman
Charity : Jurnal Pengabdian Masyarakat Vol. 6 No. 2 (2023): Charity: Jurnal Pegabdian Masyarakat
Publisher : PPM Universitas Telkom

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

Abstract

Sukaregang Garut merupakan sentra industri pengrajin dan pengusaha produk kulit lokal dengan kualitas produk kulit yang baik. Bahkan, sudah ada sejumlah tawaran kerja sama dari luar negeri yang datang ke beberapa pengrajin. Namun, karena pengolahan limbah industri kulit Sukaregang yang belum mencapai ISO, membuat pihak luar negeri urung untuk mengadakan kerja sama. Selain itu, pengetahuan desain para pengrajin produk kulit Sukaregang terutama untuk membuat desain baru yang inovatif serta keterampilan dalam memanfaatkan kemajuan IT untuk pemasaran dinilai masih lemah. Hal ini lambat laun dapat membuat produk Sukaregang akan tergeser produk luar karena masalah harga dan kualitas desain. Terkait hal tersebut, kami mengajukan program pengabdian masyarakat berupa pemberian bimbingan teknis mengenai desain produksi kulit dan pembuatan aplikasi berbasis web untuk membantu memperkenalkan serta memasarkan produk kulit Sukaregang Garut. Dengan pelaksanaan kegiatan ini, diharapkan dapat membantu meminimalisir sebagian permasalahan yang dihadapi pengrajin dan pengusaha kulit Sukaregang Garut.
Enhancing Drug-Target Affinity Prediction with Multi-scale Graph Attention Network and Attention Mechanism Yusuf, Muhammad Rizky Yusfian; Kurniawan, Isman
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 10 No. 4 (2024): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v10i4.30425

Abstract

Drug-target affinity (DTA) prediction is critical to drug discovery, yet traditional experimental methods are expensive and time-consuming. Existing computational approaches often struggle with limitations in representing the structural and sequential complexities of drugs and proteins, resulting in suboptimal prediction accuracy. This study proposes a novel framework integrating Graph Attention Networks (GAT) for drug molecular and motif graphs and Bidirectional Long Short-Term Memory (BiLSTM) for protein sequences. A two-sided multi-head attention mechanism is utilized to dynamically model drug-protein interactions, enhancing robustness and accuracy. This research contribution is the development of a robust computational model that improves the accuracy of DTA predictions, reducing dependency on traditional laboratory methods. The integration of structural and sequential features provides a more comprehensive representation of drug-protein interactions. The study utilizes the Davis and KIBA, a binding affinity datasets that is widely used. the proposed model achieving the lowest Mean Squared Error (MSE) of 0.3209 and 0.1864, the highest Concordance Index (CI) of 0.8646 and 0.8616, and the highest  of 0.5046 and 0.6672, respectively, outperforming baseline models. In conclusion, this study showed the proposed approach as a reliable method for DTA prediction, offering a faster and more accurate alternative in the drug discovery research field. However, there are still limitations, such as high computational complexity and the GAT model still uses static attention. Future work will focus on addressing this issue, testing the model across broader datasets, and implementing additional drug and target representation for richer feature extraction.
Implementation of Ant Colony Optimization – Artificial Neural Network in Predicting the Activity of Indenopyrazole Derivative as Anti-Cancer Agent Kurniawan, Isman; Kamil, Nabilla; Aditsania, Annisa; Setiawan, Erwin Budi
JOIN (Jurnal Online Informatika) Vol 8 No 1 (2023)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v8i1.1055

Abstract

Cancer is a disease induced by the abnormal growth of cells in body tissues. This disease is commonly treated by chemotherapy. However, at first, cancer cells can respond to the activity of chemotherapy over time, but over time, resistance to cancer cells appears. Therefore, it is required to develop new anti-cancer drugs. Indenopyrazole and its derivative have been investigated to be a potential drug to treat cancer. This study aims to predict indenopyrazole derivative compounds as anti-cancer drugs by using Ant Colony Optimization (ACO) and Artificial Neural Network (ANN) methods. We used 93 compounds of indenopyrazole derivative with a total of 1876 descriptors. Then, the descriptors were reduced by using the Pearson Correlation Coefficient (PCC) and followed by the ACO algorithm to get the most relevant features. We found that the best number of descriptors obtained from ACO is ten descriptors. The ANN prediction model was developed with three architectures, which are different in hidden layer number, i.e., 1, 2, and 3 hidden layers. Based on the results, we found that the model with three hidden layers gives the best performance, with the value of the R2 test, R2 train, and Q2 train being 0.8822, 0.8495, and 0.8472, respectively.
Implementation Information Gain Feature Selection for Hoax News Detection on Twitter using Convolutional Neural Network (CNN) Farid, Husnul Khotimah; Setiawan, Erwin Budi; Kurniawan, Isman
Indonesian Journal on Computing (Indo-JC) Vol. 5 No. 3 (2020): December, 2020
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34818/INDOJC.2020.5.3.506

Abstract

The development of information and communication technology is currently increased, especially related to social media. Nowadays, many people get information through social media, especially Twitter, because of its easy access and it doesn't cost much. However, it has a negative impact in the form of spreading fake news or hoaxes that are difficult to detect. In this research, the authors developed a hoax news detection model using the Convolutional Neural Network and the TF-IDF weighting method. Feature selection is performed using Information Gain with various features, such as unigram, bigram, trigram and a combination of the three. Testing is done with 3 scenarios, classification, classification by weighting, classification by weighting and feature selection. The parameter used in the information gain feature selection is the threshold 0.8. The results showed that the classification by weighting and feature selection produced the highest accuracy that is equal to 95.56% on the unigram + bigram features with a comparison of training data and test data 50:50.
Co-Authors Ali Assegaf Annisa Aditsania Ardannur Deswanto, Farrel Ardhyka Dewantara Arionmaro Asi Simaremare Arsytania, Ihsani Hawa Azizah, Kholishoh Nur Bambang Hadi Prakoso Crisanadenta Wintang Kencana Danang Triantoro Murdiansyah Delfina, Haura Adzkia Deni Saepudin Devi Munandar Didit Adytia Diya Namira Purba Ekky Wicaksana Erwin Budi Setiawan Fadhli Rahman Farid, Husnul Khotimah Farid, Mochammad Rafi Fhira Nhita Furqon Hidayat Husnul Khotimah Farid Hutama, Nanda Yonda I Kadek Andrean Pramana Putra Pramana Iljannah, Nissa Indra A Aditya Jayanata, Wisnu Kamil, Nabilla Kemas Muslim Lhaksmana Khoerunnisa, Ghina Komang Triolascarya Lela Lailatul Khumaisah Lela Mukmilah Yuningsih Lusi Sofiana Silaban Maya Rosalinda Mela Mai Anggraini Mohammad Adietya Perdana Nabilla Kamil Nanda Yonda Hutama Nurul Ikhsan Reina Wardhani Reza Fauzi Iskandar Reza Rendian Septiawan Rian Febrian Umbara Rizki Amanullah Hakim Rizki, Alfath Muhammad Salsabil, Adinda Arwa Sapitri, Nazwa Martina Saut Sihol Ritonga Wicaksana, Ekky Wisesty , Untari Novia Yusuf, Muhammad Rizky Yusfian