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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Bulletin of Electrical Engineering and Informatics JOIN (Jurnal Online Informatika) JURNAL MEDIA INFORMATIKA BUDIDARMA JITK (Jurnal Ilmu Pengetahuan dan Komputer) Justek : Jurnal Sains Dan Teknologi Building of Informatics, Technology and Science Jurnal Informatika dan Rekayasa Elektronik Indonesian Journal of Electrical Engineering and Computer Science TIN: TERAPAN INFORMATIKA NUSANTARA Jurnal Teknik Informatika (JUTIF) Jurnal Pengabdian Kepada Masyarakat (JPKM) Tabikpun Jurnal Data Mining dan Sistem Informasi Jurnal Ilmiah Infrastruktur Teknologi Informasi Jurnal Teknologi dan Sistem Informasi Journal Social Science And Technology For Community Service Jurnal Informatika Teknologi dan Sains (Jinteks) Journal of Engineering and Information Technology for Community Service Jurnal Ilmiah Informatika dan Ilmu Komputer Jurnal Media Borneo Jurnal Informatika: Jurnal Pengembangan IT
Nirwana Hendrastuty
Universitas Teknokrat Indonesia
Aerospace Engineering Arts Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Social Sciences Veterinary Other

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Prediction of Indonesian Inflation Rate Using Regression Model Based on Genetic Algorithms Dharma, Faisal; Shabrina, Shabrina; Noviana, Astrid; Tahir, Muhammad; Hendrastuty, Nirwana; Wahyono, Wahyono
JOIN (Jurnal Online Informatika) Vol 5 No 1 (2020)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v5i1.532

Abstract

Inflation occurs where there is an increase in the price of goods or services in general and continuously in a country. Uncontrolled inflation will have an impact on the decline of the Indonesian economy. Therefore, the prediction of future inflation levels is necessary for the government to develop economic policies in the future. Prediction of inflation levels can be done by studying historical past Consumer Price Index (CPI) data. Regression methods are often used to solve prediction problems. The problem of finding the optimal prediction model can be seen as an optimization problem. Genetic algorithms are often used to deal with optimization problems. Thus, this work proposed to use a genetic algorithm-based regression model for predicting inflation levels. The model was trained and evaluated using real CPI data which obtained from the Indonesian Central Bank. Based on the experiment, it is proved that the proposed model is effective in predicting the inflation level as it gains MSE of 0.1099.
COMPARISON OF NAÏVE BAYES ALGORITHM AND SUPPORT VECTOR MACHINE IN SENTIMENT ANALYSIS OF BOYCOTT ISRAELI PRODUCTS ON TWITTER Hayurian, Laisha Amilna; Hendrastuty, Nirwana
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 3 (2024): JUTIF Volume 5, Number 3, June 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.3.1813

Abstract

The Israeli-Palestinian conflict has captured the attention of Indonesians and even the world for decades, with the death toll reaching 17,000 Palestinians. Indonesians have expressed various opinions, including a proposed boycott of products that allegedly support Israel as a form of protest against the ongoing conflict. This study explores the opinions and sentiments of the Indonesian people regarding the Israel-Palestine conflict and the efforts to boycott Israeli products on social media twitter. This study aims to compare the accuracy of the two algorithms in classifying sentiment towards boycotting Israeli products. A total of 2288 comment data were processed using the Naïve Bayes and Support Vector Machine (SVM) algorithm classification methods. The results show that the Naïve Bayes algorithm has higher accuracy with a data division ratio of 70:30 and 80:30 for training data and testing data. Accuracy results with 70:30 data division reached 84% using the Naïve Bayes algorithm model, while the SVM algorithm model reached 78%. And the accuracy results with 80:20 data division reached 85% using the Naïve Bayes algorithm model, with the SVM algorithm model reaching 84%. This study provides an understanding of the concept of text mining and data mining and can be a reference for similar research.
APPLICATION OF CANNY OPERATOR IN BATIK MOTIF IMAGE CLASSIFICATION WITH CONVOLUTIONAL NEURAL NETWORK APPROACH Iwan Jaya Bakti; Hendrastuty, Nirwana
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 3 (2024): JUTIF Volume 5, Number 3, June 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.3.1894

Abstract

Batik, as Indonesia's cultural heritage, has high artistic value and has a variety of unique motifs.. The main focus of this research is to solve the problem of the complexity and diversity of motifs found in Indonesian batik culture. The Canny operator is used as a first step to extract the edges of batik motifs, with the aim of improving the quality of feature extraction before entering the classification stage using CNN, specifically by using the DenseNet121 model. The dataset of this study was obtained through the Kaggle platform, published by Dionisius Darryl Hermansyah. The platform consists of 983 images (.jpg) with 20 different Indonesian batik motifs. Pre-processing includes the use of Canny for edge detection and data augmentation to increase the diversity of the dataset. Next, variations in the number of epochs and batch size were used to train the model. The results show that in the first test, the use of the Canny operation gives a higher confidence level in the model. In the model with Canny, there is a 1.6% increase in accuracy (33.57% with Canny and 31.97% without Canny). In addition, there are differences in the level of confidence in some batik classes. For example, the "batik mega mendung" class shows an increase in confidence of 66.57% with Canny (88.53% with Canny and 21.96% without Canny), while the "batik sekar" class shows a decrease in confidence of 12.09% with Canny.
THE INFLUENCE OF FEATURE EXTRACTION ON AUTOMATIC TEXT SUMMARIZATION USING GENETIC ALGORITHM Rahmadianti, Fitrah Amalia; Hendrastuty, Nirwana
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 4 (2024): JUTIF Volume 5, Number 4, August 2024 - SENIKO
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.4.2064

Abstract

Text summarization using extraction methods is a technique that summarizes by retaining a subset of sentences to create a summary. There are two types of documents commonly used for summarization: single document and multi-document. Multi-document refers to documents originating from one or more sources that contain several main ideas. The data used in this research is obtained from the E-lapor DIY website, consisting of 1000 data entries. E-Lapor DIY is a website provided by the DIY government to accommodate all public aspirations and complaints, such as damaged roads, broken traffic lights, insufficient street lighting, litter in public places, and more. The accumulation of data and the delayed response time has become an issue for the government in addressing these complaints. This research aims to consider the impact of using feature extraction for text summarization using genetic algorithms. The feature extraction compared in this research is the influence of sentence position in feature extraction. The results obtained show that Precision testing using F1 is 0.64, and without using F1, it is 0.66. Recall testing using F1 is 0.65, and without using F1, it is 0.68. F-Measure testing using F1 is 0.65, and without using F1, it is 0.68. This testing using the algorithm can be an interesting alternative for more time-efficient text summarization.
Penerapan Data Mining Menggunakan Algoritma K-Means Clustering Dalam Evaluasi Hasil Pembelajaran Siswa Hendrastuty, Nirwana
Jurnal Ilmiah Informatika dan Ilmu Komputer (JIMA-ILKOM) Vol. 3 No. 1 (2024): Volume 3 Number 1 March 2024
Publisher : PT. SNN MEDIA TECH PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58602/jima-ilkom.v3i1.26

Abstract

Evaluation of student learning outcomes is a critical process in education that aims to measure the achievement of learning objectives. Through various methods such as tests, projects, and observations, teachers can assess students' understanding, skills, and progress in the subject matter. The purpose of applying data mining using the K-Means Clustering algorithm in evaluating student learning outcomes is to identify patterns that may be hidden in learning outcome data, divide students into groups based on their level of achievement or learning characteristics, and provide valuable insights to teachers and education stakeholders. The results of clustering student learning assessment data can uncover patterns that are beneficial to educators and school administrators. Analysis of these clusters can reveal information about achievement trends, trends in success or difficulty in specific subjects, as well as allow identification of students who need additional help. Grouping of cluster results based on student assessment data with k-means obtained 2 groups of students, namely Diligent students with group C0 and group students Very Diligent with group C1. The C0 group of Diligent students consists of 63 students and the C1 group consists of 91 Very Diligent students. The silhouette score test results for cluster 2 are as high as 0.9168 and show that grouping data into these groups is better, the use of silhouette score as an evaluation metric provides useful guidance in determining the optimal number of clusters in clustering analysis and data interpretation.
Performance evaluation of feature extraction to improve the classification of PTM in C-glycosylation using XGBoost Damayanti, Damayanti; Rosyking Lumbanraja, Favorisen; Junaidi, Akmal; Sutyarso, Sutyarso; Nugroho Susanto, Gregorius; Hendrastuty, Nirwana
Bulletin of Electrical Engineering and Informatics Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i2.8466

Abstract

Protein function is regulated by an important mechanism known as post-translational modification (PTM). Covalent and enzymatic protein modifications are added during protein biosynthesis, and such alterations significantly influence the regulation of gene activity and the functionality of proteins. Glycosylation, one type of PTM, involves adding sugar groups to a protein's structure. Numerous illnesses, such as diabetes, cancer, and the flu, have been linked to glycosylation. Therefore, it is critical to predict the presence of glycosylation, whether it occurs or not. Currently, predicting glycosylation sites is still done manually using biological methods, which require repeated experiments and a significant amount of time. To address these challenges, it is essential to rapidly develop computational data models using machine learning methods. In this study, the extreme gradient boosting (XGBoost) method is implemented, and C-glycosylation data is obtained from the publicly accessible UniProt website. The objective is to enhance the accuracy of C-glycosylation prediction using the XGBoost method. Feature extraction is performed using amino acid index (AAindex), composition, transition, and distribution (CTD), solvent AccessiBiLitiEs (SABLE), hydrophobicity, and pseudo amino acid composition (PseAAC) to improve accuracy. The minimum redundancy maximum relevance (MRMR) method is applied for feature selection. The findings of the study demonstrate that the PTM C-glycosylation prediction achieved 100%.
REFORMULATION OF MULTI-ATTRIBUTE UTILITY THEORY NORMALIZATION TO HANDLE ASYMMETRIC DATA IN MADM Puspaningrum, Ajeng Savitri; Susanto, Erliyan Redy; Hendrastuty, Nirwana; Setiawansyah, Setiawansyah
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 2 (2025): JITK Issue November 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i2.7273

Abstract

Multi-Attribute Utility Theory (MAUT) is a widely used multi-attribute decision-making (MADM) method due to its ability to integrate multiple criteria into a single utility value. However, conventional MAUT faces limitations when handling asymmetric data, where standard normalization processes often lead to value distortion and less representative rankings. This study aims to reformulate the normalization function in MAUT to improve adaptability to non-symmetric data distributions and to enhance ranking validity in decision-making. A modification approach called MAUT-A was developed by applying an adaptive normalization mechanism capable of accommodating extreme distributions and outliers by adding Z-score normalization. The performance of MAUT-A was evaluated by comparing the correlation of its ranking results with reference rankings, and the outcomes were benchmarked against conventional MAUT. The experimental findings indicate that conventional MAUT achieved a correlation value of 0.9688 with the reference ranking, while the proposed MAUT-A method achieved a higher correlation of 0.9792. This improvement represents that MAUT-A has better suitability, stability, and reliability in managing asymmetric data. The study contributes by offering a reformulated MAUT framework through adaptive normalization, providing more accurate, stable, and fair ranking outcomes. This approach enhances the validity of MADM applications, particularly in contexts involving asymmetric data distributions
Combination of MOORA and ITARA Methods in Decision Support Systems for Measuring the Performance of Quality Control Teams Hendrastuty, Nirwana; Wang, Junhai; Sulistiyawati, Ari; Darwis, Dedi; Setiawansyah, Setiawansyah; Jumaryadi, Yuwan; Sumanto, Sumanto
TIN: Terapan Informatika Nusantara Vol 6 No 6 (2025): November 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i6.8382

Abstract

The problems that often arise in evaluating the performance of the Quality Control team are the subjectivity in determining the weight of criteria and the limitations of traditional methods in producing objective and consistent rankings. To address this issue, this research integrates the Indifference Threshold-based Attribute Ratio Analysis (ITARA) and Multi-Objective Optimization on the basis of Ratio Analysis (MOORA) methods within a decision support system. The ITARA method is used to determine the weights of criteria based on data variation, making them more representative of real conditions, with the result that Accuracy of Product Defect Identification becomes the most dominant criterion with a weight of 0.3999, followed by Response Speed to Issues at 0.1877, while other criteria have lower weights. Furthermore, the MOORA method is used to calculate the preference of alternatives, resulting in a final ranking. The analysis results indicate that the Quality Assurance Team ranks first, followed by the Quality Improvement Team in second place, while the Quality Inspection Team is in the last position. To test the reliability of the model, a sensitivity analysis was conducted by varying the weights of the main criteria. The results show that the ranking structure is relatively stable, with changes only occurring in the positions of the first and second ranks when the accuracy weight is reduced by 0.2. In conclusion, the combination of ITARA-MOORA proves to be capable of producing objective, robust, and reliable performance evaluations as a basis for strategic decision-making in enhancing the quality of the quality control teams.
REFORMULATION OF MULTI-ATTRIBUTE UTILITY THEORY NORMALIZATION TO HANDLE ASYMMETRIC DATA IN MADM Puspaningrum, Ajeng Savitri; Susanto, Erliyan Redy; Hendrastuty, Nirwana; Setiawansyah, Setiawansyah
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 2 (2025): JITK Issue November 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i2.7273

Abstract

Multi-Attribute Utility Theory (MAUT) is a widely used multi-attribute decision-making (MADM) method due to its ability to integrate multiple criteria into a single utility value. However, conventional MAUT faces limitations when handling asymmetric data, where standard normalization processes often lead to value distortion and less representative rankings. This study aims to reformulate the normalization function in MAUT to improve adaptability to non-symmetric data distributions and to enhance ranking validity in decision-making. A modification approach called MAUT-A was developed by applying an adaptive normalization mechanism capable of accommodating extreme distributions and outliers by adding Z-score normalization. The performance of MAUT-A was evaluated by comparing the correlation of its ranking results with reference rankings, and the outcomes were benchmarked against conventional MAUT. The experimental findings indicate that conventional MAUT achieved a correlation value of 0.9688 with the reference ranking, while the proposed MAUT-A method achieved a higher correlation of 0.9792. This improvement represents that MAUT-A has better suitability, stability, and reliability in managing asymmetric data. The study contributes by offering a reformulated MAUT framework through adaptive normalization, providing more accurate, stable, and fair ranking outcomes. This approach enhances the validity of MADM applications, particularly in contexts involving asymmetric data distributions
Rancang Bangun Aplikasi Monitoring Santri Berbasis Android (Studi Kasus: Pesantren Nurul Ikhwan Maros) Hendrastuty, Nirwana
Jurnal Data Mining dan Sistem Informasi Vol 2, No 2 (2021): Agustus 2021
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/jdmsi.v2i2.1346

Abstract

Pesantren merupakan lembaga pendidikan, yang menampung peserta didik dan dibina agar memiliki kemampuan, kecerdasan, dan keterampilan sehingga tercapainya tujuan pendidikan. Namun selama ini pengawasan terhadap santri sebagian besar hanya dilakukan oleh pihak pondok. Informasi tentang perkembangan proses belajar santri biasanya hanya diterima orang tua sekali dalam satu semester yakni saat terima raport. Biasanya pelangaran yang dilakukan meliputi absensi, nilai, keterlambatan pembayaran uang SPP, dan kasus-kasus pelanggaran lain yang dilakukan santri. Hal ini tentu tidak memberikan banyak solusi karena orang tua tidak memberikan pengawasan sejak awal.Penelitian ini bertujuan untuk membangun aplikasi monitoring siswa berbasis android. Sehingga orang tua atau wali dari santri dapat melihat perkembangan anaknya dengan cara mengasukkan nomor induk siswa nasional (NISN) sebagai username dan pasword yang harus dimasukan berupa tanggal lahir santri dengan format dd/mm/yyyy.  Metode pengumpulan data yang digunakan pada penelitian ini menggunakan metode observasi yaitu mengamati secara langsung proses pengolahan akademik di sekolah dan metode studi pustaka dengan mempelajari referensi  buku, artikel  dan   internet   yang berhubungan    dengan    aplikasi   smartphone berbasis android dalam me-monitoring santri. Rancangan sistem yang digunakan pada penelitian ini berbasis OOP yaitu menggunakan UML. Rancangan UML yang dibangun adalah Usecase Diagram, Sequence Diagram, Activity diagram dan Class diagram.
Co-Authors Ade Dwi Putra aditya hidayat Akmal Junaidi Algho Niyu Behainksa Ali, Humaidi Andre Wijaya Anton Mahendra An’ars, M. Ghufroni Apriani, Linda Ari Sulistiyawati aryadi, bagas putra Azhari SN Bagus Bramantyo Chris Mario Damayanti Damayanti Damayanti, Damayanti Dedi Darwis Dharma, Faisal Dimas Elang Setyoko Ely Kurniawan Erliyan Redy Susanto Fadillah, Betran Dwi Fitrah Amalia Fitrah Amalia Rahmadianti Hayurian, Laisha Amilna Heni Sulistiani Heru Setiawan Intan Permata Sari Isnain, Auliya Rahman Iwan Jaya Bakti Jumaryadi, Yuwan Lili Andraini Lumbanraja, Favorisen R M Ghufroni An'ars M. Ghufroni An'ars M. Ghufroni An'ars M. Ghufroni An’ars M. Tova Maulida Waya Inayah Meutia Kartika Arisandi Muhammad Pajar Kharisma Putra Muhammad Tahir Noviana, Astrid Nugroho Susanto, Gregorius Nurhidayat, Rifki Paradhita, Astrid Noviana Prasatya, Agung Pusparisti, Myrtana Rahmadianti, Fitrah Amalia Rahman, Miftahur Ramadhan, Naufal Rizqi Rani, Arum Mustika Samuel Hutagalung Setiawansyah Setiawansyah Setyawati Setyawati Shabrina Shabrina, Shabrina Styawati Styawati Sumanto, Sumanto Susanto, Erliyan Redy Sutyarso Sutyarso Very Hendra Saputra Wahyono Wahyono Wahyudi, Agung Deni Wang, Junhai Wayan Darlin Wisnu Mukti Dwiyudho Putro Wita Kurnia Yunanda, Vinsensius Dendi Yuniar, Eny