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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Techno.Com: Jurnal Teknologi Informasi Elkom: Jurnal Elektronika dan Komputer Bulletin of Electrical Engineering and Informatics Prosiding Seminar Nasional Sains Dan Teknologi Fakultas Teknik Journal of Telematics and Informatics INFOKAM Sisforma: Journal of Information Systems CESS (Journal of Computer Engineering, System and Science) Proceeding SENDI_U Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Rekam Medis dan Informasi Kesehatan Media Ilmu Kesehatan Jurnal Teknik Informatika UNIKA Santo Thomas J-SAKTI (Jurnal Sains Komputer dan Informatika) Jesya (Jurnal Ekonomi dan Ekonomi Syariah) JOURNAL OF SCIENCE AND SOCIAL RESEARCH Jurnal Riset Informatika Jurnal Abdimas PHB : Jurnal Pengabdian Masyarakat Progresif Humanis Brainstorming SOSCIED Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Jurnal Ilmiah Intech : Information Technology Journal of UMUS Tematik : Jurnal Teknologi Informasi Komunikasi Journal of Computer Networks, Architecture and High Performance Computing Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Journal of Business and Technology J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Teknik Informatika Unika Santo Thomas (JTIUST) Jurnal Pengabdian Masyarakat Intimas (Jurnal INTIMAS): Inovasi Teknologi Informasi Dan Komputer Untuk Masyarakat Jurnal: International Journal of Engineering and Computer Science Applications (IJECSA) STORAGE: Jurnal Ilmiah Teknik dan Ilmu Komputer Seminar Nasional Ilmu Terapan Jurnal Kabar Masyarakat Journal of Computing Theories and Applications Jurnal Informatika: Jurnal Pengembangan IT Journal of Future Artificial Intelligence and Technologies Proceeding of The International Conference on Mathematical Sciences, Natural Sciences, and Computing
Dian Kristiawan Nugroho
Universitas Stikubank
Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Public Health Social Sciences Other

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Perancangan Model Deteksi Potensi Siswa Putus Sekolah Menggunakan Metode Logistic Regression Dan Decision Tree Ermillian, Ade; Nugroho, Kristiawan
Jurnal Informatika: Jurnal Pengembangan IT Vol 9, No 3 (2024)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v9i3.8007

Abstract

The phenomenon of student dropouts is one of the main challenges in education, influenced by various factors such as absenteeism, economic pressures on families, low academic performance, and lack of motivation. This issue not only affects the personal development of students but also tarnishes the reputation of educational institutions. Therefore, an innovative technology-based approach, such as data mining, is needed to detect students at risk of dropping out early. This study aims to design a model for detecting the potential of school dropout students using Logistic Regression and Decision Tree methods based on student data from SMA N 4 Tegal. The variables used in the analysis include demographic, academic, and social information such as absenteeism, average semester grades, parental income, and transportation type. The dataset is processed using one-hot encoding and label encoding techniques to convert categorical data into numeric values. The results indicate that both methods have their respective advantages. The Decision Tree model achieves high precision, especially in predicting students who continue their education, with a precision of 0.99 for the "Continue School" class. However, recall for the "Dropout" class remains low (0.60), indicating the need for improvements in detecting students at risk of dropping out. On the other hand, the Logistic Regression model shows better balance in detecting both classes, with more balanced accuracy and recall. This study concludes that both models can be used to monitor the potential of school dropouts and provide data-driven recommendations for more accurate educational decision-making.
Integrating SMOTE-Tomek and Fusion Learning with XGBoost Meta-Learner for Robust Diabetes Recognition Setiadi, De Rosal Ignatius Moses; Nugroho, Kristiawan; Muslikh, Ahmad Rofiqul; Iriananda, Syahroni Wahyu; Ojugo, Arnold Adimabua
Journal of Future Artificial Intelligence and Technologies Vol. 1 No. 1 (2024): June 2024
Publisher : Future Techno Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/faith.2024-11

Abstract

This research aims to develop a robust diabetes classification method by integrating the Synthetic Minority Over-sampling Technique (SMOTE)-Tomek technique for data balancing and using a machine learning ensemble led by eXtreme Gradient Boosting (XGB) as a meta-learner. We propose an ensemble model that combines deep learning techniques such as Bidirectional Long Short-Term Memory (BiLSTM) and Bidirectional Gated Recurrent Units (BiGRU) with XGB classifier as the base learner. The data used included the Pima Indians Diabetes and Iraqi Society Diabetes datasets, which were processed by missing value handling, duplication, normalization, and the application of SMOTE-Tomek to resolve data imbalances. XGB, as a meta-learner, successfully improves the model's predictive ability by reducing bias and variance, resulting in more accurate and robust classification. The proposed ensemble model achieves perfect accuracy, precision, recall, specificity, and F1 score of 100% on all tested datasets. This method shows that combining ensemble learning techniques with a rigorous preprocessing approach can significantly improve diabetes classification performance.
Analisis Penerimaan Teknologi Aplikasi Pemesanan Makanan Gofood dengan Technology Acceptance Model dan Pearson Correlation Munna, Aliyatul; Nugroho, Kristiawan; Hadiono, Kristophorus
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 7, No 2 (2023): EDISI SEPTEMBER
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v7i2.682

Abstract

Technology has proven itself as a powerful tool to ease human work in many ways, including food ordering technology. GoFood is a popular and innovative food ordering application that has brought convenience and comfort to users in Indonesia. This research aims to analyze the technology acceptance of the Gofood food ordering application using the Technology Acceptance Model (TAM). TAM is a framework used to understand the factors that influence the acceptance and use of technology. In the context of food ordering apps, user acceptance of the app is critical to the success and growth of the business. This research method involves collecting data through online surveys among Gofood application users. Respondents were asked to assess relevant factors in the TAM, including perceived usefulness, perceived ease of use, as well as attitudes toward use and behavioral intention to use. ), and test the correlation between constructs using Pearson correlation. The results of the analysis show that these findings indicate that perceived usefulness and perceived ease of use of the GoFood application contribute to attitudes toward use and interest in utilizing and using the application. .
Analisis Penerimaan Teknologi Aplikasi Pemesanan Makanan Gofood dengan Technology Acceptance Model dan Pearson Correlation Munna, Aliyatul; Nugroho, Kristiawan; Hadiono, Kristophorus
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 7, No 2 (2023): EDISI SEPTEMBER
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v7i2.682

Abstract

Technology has proven itself as a powerful tool to ease human work in many ways, including food ordering technology. GoFood is a popular and innovative food ordering application that has brought convenience and comfort to users in Indonesia. This research aims to analyze the technology acceptance of the Gofood food ordering application using the Technology Acceptance Model (TAM). TAM is a framework used to understand the factors that influence the acceptance and use of technology. In the context of food ordering apps, user acceptance of the app is critical to the success and growth of the business. This research method involves collecting data through online surveys among Gofood application users. Respondents were asked to assess relevant factors in the TAM, including perceived usefulness, perceived ease of use, as well as attitudes toward use and behavioral intention to use. ), and test the correlation between constructs using Pearson correlation. The results of the analysis show that these findings indicate that perceived usefulness and perceived ease of use of the GoFood application contribute to attitudes toward use and interest in utilizing and using the application. .
Aspect-Based Sentiment Analysis on E-commerce Reviews using BiGRU and Bi-Directional Attention Flow Setiadi, De Rosal Ignatius Moses; Warto, Warto; Muslikh, Ahmad Rofiqul; Nugroho, Kristiawan; Safriandono, Achmad Nuruddin
Journal of Computing Theories and Applications Vol. 2 No. 4 (2025): JCTA 2(4) 2025
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.12376

Abstract

Aspect-based sentiment Analysis (ABSA) is vital in capturing customer opinions on specific e-commerce products and service attributes. This study proposes a hybrid deep learning model integrating Bi-Directional Gated Recurrent Units (BiGRU) and Bi-Directional Attention Flow (BiDAF) to perform aspect-level sentiment classification. BiGRU captures sequential dependencies, while BiDAF enhances attention by focusing on sentiment-relevant segments. The model is trained on an Amazon review dataset with preprocessing steps, including emoji handling, slang normalization, and lemmatization. It achieves a peak training accuracy of 99.78% at epoch 138 with early stopping. The model delivers a strong performance on the Amazon test set across four key aspects: price, quality, service, and delivery, with F1 scores ranging from 0.90 to 0.92. The model was also evaluated on the SemEval 2014 ABSA dataset to assess generalizability. Results on the restaurant domain achieved an F1-score of 88.78% and 83.66% on the laptop domain, outperforming several state-of-the-art baselines. These findings confirm the effectiveness of the BiGRU-BiDAF architecture in modeling aspect-specific sentiment across diverse domains.
Penerapan Metode Adaptive Boosting Pada Analisis Sentimen Kenaikan BBM Pertamina Faizi, Aditya Wahyu Nur; Nugroho, Kristiawan
Jurnal Teknik Informatika UNIKA Santo Thomas Vol 8 No. 2 : Tahun 2023
Publisher : LPPM UNIKA Santo Thomas

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

Abstract

Masyarakat selalu menentang kenaikan harga BBM. Selain itu, emosi negatif semakin terlihat karena kisah di media sosial sering dipenuhi dengan provokasi yang berlebihan. Dianggap bahwa pengalihan subsidi BBM oleh pemerintah tidak menguntungkan rakyat. Kenaikan harga BBM juga memicu interaksi dan percakapan warganet Indonesia di media sosial. Perbincangan isu kenaikan BBM di media sosial terdapat 403.700 perbincangan dengan 2,2 juta interaksi antar pengguna media sosial. Data penelitian mencakup 560 tweet. Ini dibagi menjadi dua, 500 untuk data latihan dan 60 untuk data uji, yang disimpan dalam format xlsx. Algoritma yang di gunakan adalah AdaBoost dengan klasifikasi sentimen positif atau negatif. Studi ini menghasilkan algoritma AdaBoost memiliki kemampuan untuk mengkategorikan tweet kenaikan BBM Pertamina ke dalam kelas bersentimen positif atau negatif dengan akurasi sebesar 86,8%.
Penerapan Metode Adaptive Boosting Pada Analisis Sentimen Kenaikan BBM Pertamina Faizi, Aditya Wahyu Nur; Nugroho, Kristiawan
Jurnal Teknik Informatika UNIKA Santo Thomas Vol 8 No. 2 : Tahun 2023
Publisher : LPPM UNIKA Santo Thomas

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

Abstract

Masyarakat selalu menentang kenaikan harga BBM. Selain itu, emosi negatif semakin terlihat karena kisah di media sosial sering dipenuhi dengan provokasi yang berlebihan. Dianggap bahwa pengalihan subsidi BBM oleh pemerintah tidak menguntungkan rakyat. Kenaikan harga BBM juga memicu interaksi dan percakapan warganet Indonesia di media sosial. Perbincangan isu kenaikan BBM di media sosial terdapat 403.700 perbincangan dengan 2,2 juta interaksi antar pengguna media sosial. Data penelitian mencakup 560 tweet. Ini dibagi menjadi dua, 500 untuk data latihan dan 60 untuk data uji, yang disimpan dalam format xlsx. Algoritma yang di gunakan adalah AdaBoost dengan klasifikasi sentimen positif atau negatif. Studi ini menghasilkan algoritma AdaBoost memiliki kemampuan untuk mengkategorikan tweet kenaikan BBM Pertamina ke dalam kelas bersentimen positif atau negatif dengan akurasi sebesar 86,8%.
Analisis Penerimaan Teknologi dan Dampaknya pada Kinerja Pegawai di PLTU: Kajian dengan Model UTAUT-TTF Muhamad Riski Atarik; Nugroho, Kristiawan
TEMATIK Vol. 12 No. 1 (2025): Tematik : Jurnal Teknologi Informasi Komunikasi (e-Journal) - Juni 2025
Publisher : LPPM POLITEKNIK LP3I BANDUNG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38204/tematik.v12i1.2231

Abstract

Pembangkit Listrik Tenaga Uap (PLTU) merupakan sistem pembangkit listrik yang berperan penting dalam memenuhi kebutuhan energi, namun menghadapi tantangan seperti kendala infrastruktur, keterbatasan sumber daya manusia, dan resistensi terhadap teknologi baru. Salah satu inovasi yang diperkenalkan untuk mengatasi masalah tersebut adalah Performance Monitoring System (PMS). Penelitian ini bertujuan mengevaluasi kinerja pegawai dan menganalisis penerimaan pengguna terhadap PMS di PLTU, guna menemukan solusi yang dapat meningkatkan efisiensi operasional dan pemanfaatan teknologi. Monitoring kinerja dianggap penting untuk meningkatkan efisiensi operasional, transparansi, dan pengambilan keputusan berbasis data. Pendekatan kuantitatif digunakan dengan mengadopsi dua model teoritis: Unified Theory of Acceptance and Use of Technology (UTAUT) untuk mengevaluasi penerimaan teknologi, serta Task Technology Fit (TTF) untuk menilai kesesuaian tugas dengan teknologi. Data dikumpulkan melalui survei kuesioner kepada pegawai pengguna PMS. Hasil menunjukkan bahwa ekspektasi kinerja dan ekspektasi usaha dalam model UTAUT secara signifikan memengaruhi penerimaan pengguna terhadap PMS. Analisis TTF mengungkap bahwa kesesuaian antara tugas dan teknologi meningkatkan efisiensi kerja serta kinerja pegawai. Penelitian ini memberikan kontribusi dalam memahami bagaimana penerimaan teknologi dan kesesuaian tugas-teknologi memengaruhi kinerja, sehingga mendukung keberhasilan operasional PLTU.
AHP–SAW-Based Decision Support System for Culinary Tourism Restaurant Selection in Semarang City Cahaya, Agus Indra; Aprico, Fikky; Apriyanti, Dewi; Nugroho, Kristiawan; Ardhianto, Eka
Journal of Computer Networks, Architecture and High Performance Computing Vol. 7 No. 3 (2025): Articles Research July 2025
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v7i3.6557

Abstract

Semarang City boasts a diverse array of appealing culinary restaurants, yet both tourists and local residents frequently encounter difficulties in selecting dining establishments that best match their preferences, often diminishing their culinary tourism experience and leading to inefficient time usage. This research aims to develop a decision support model by implementing a combined approach of the Analytic Hierarchy Process (AHP) and Simple Additive Weighting (SAW) to streamline the restaurant selection process. AHP was selected for its capability to establish hierarchical criteria weights, while SAW offers an efficient method for ranking alternatives. The initial phase utilized AHP to determine the weights of six identified relevant primary criteria: Cleanliness, Location, Price, Ambiance, Taste, and Rating. Assessments for each of these criteria were gathered through surveys administered to 30 culinary enthusiasts in Semarang City. Subsequently, the second phase employed SAW to calculate the final scores for 10 culinary tourism restaurants in Semarang, evaluated through questionnaires by 10 different respondents. The calculation results placed Folkcafe at ALVA in the first rank (with a score of 0.8656), followed by Ikan Bakar Cianjur in the second rank (score of 0.8524), and Pelangi Cheese Chiffon Cake in the third rank (score of 0.8173). These findings unequivocally demonstrate the effectiveness of applying AHP and SAW for prioritizing culinary restaurants in Semarang, further supported by the valid consistency of the AHP criteria weights (CR = 0.0341). This study contributes to the DSS literature by combining AHP and SAW in the underexplored context of culinary tourism ranking. This model is expected to serve as a practical guide for visitors and a foundational basis for the development of digital recommendation systems within the culinary tourism sector.
Perbandingan Metode SAW dan TOPSIS untuk Pemeringkatan IPM di Jawa Tengah Tahun 2024 Kristiyono, Budi; Alfiqhyanto, Damas; Ariyanto, Eko; Nugroho, Kristiawan; Ardhianto, Eka
Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Vol 10, No 2 (2025): Edisi Agustus
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/jurasik.v10i2.908

Abstract

The Human Development Index (HDI) is an important indicator for assessing the success of regional development, particularly in the aspects of health, education, and economy. However, as an aggregate indicator, HDI cannot yet specifically represent the unique development needs of each region. Therefore, a decision support system (DSS) approach is needed to analyze HDI components more deeply and objectively. This study aims to compare two multicriteria decision-making methods-Simple Additive Weighting (SAW) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS)-in ranking the HDI of regencies/cities in Central Java Province in 2024. The HDI data was obtained from the Central Statistics Agency (BPS) 2024, covering the three main dimensions of HDI. The analysis results show differences in ranking orders between the two methods, indicating that the choice of analytical method can influence development interpretations and priorities. This study makes a significant contribution by providing a data-driven approach to support more targeted regional development policies.
Co-Authors Achmad Nuruddin Safriandono Afandi , Afandi Afif, Randi Ahmad Fathoni Ajib Susanto Ajie, Ach. Ridlo Bayu Alex Chandra Iswanto Alfiqhyanto, Damas Aminudin, Agus Anjis Sapto Nugroho Anton Sujarwo Anton Sujarwo Aprico, Fikky Apriyanti, Dewi Aquinia, Ajeng Araaf, Mamet Adil Arsyad , Muhammad Rafi Haidar budi hartono Budiarto, Indri Cahaya, Agus Indra De Rosal Ignatius Moses Setiadi Dhendra Marutho Dwi Agus Diartono Dwi Budi Santoso Edy Winarno Eka Ardhianto Eko Ariyanto Eko Prasetyo Eko Prasetyo Eksawati, Rini Endang Tjahjaningsih Eri Zuliarso Ermillian, Ade Faizi, Aditya Wahyu Nur fakhri Farooq, Omar Fitrianto, Lindu Hari Murti Hermawan, Taufan Hidayat, Suluh Irawan, Sandy Islam, Hussain Md Mehedul Isworo Nugroho Kasmari . Kirana, Heni Candra Kristhoporus Hadiono Kristianto, Taufik Fredy Kristiyono, Budi Kristophorus Hadiono Lie Liana Lie Liana . Minantri Haika, Shara Muh Kholid Rizky Sapawi Muhamad Riski Atarik Mulyani , Wahyu Sri Mulyo Budi Setiawan Munna, Aliyatul Muslikh, Ahmad Rofiqul Niken Puspitasari Nurmakhlufi, Alfin Ojugo, Arnold Adimabua Omar Farooq Palupi, Dian Perdana, Willy Yudha Prabowo, Ardian Adi Prihatin, Rudi Setyo Rachman, Rahadian Kristiyanto Raden Mohamad Herdian Bhakti Radyanto, Mohammad Riza Rahadiyanto, Cahyono Raharjo, Fajar Retnowati Rokhayadi, Wakhid Ruslana, Zauyik Nana Saputra, Roni Halim Saputro, Risky Wisnu Sariyun Naja Anwar Sarwo Edi, Sarwo Setyaningtyas, Elvanita Sri Mulyani Sugeng Murdowo Suhana Suhana Sulastri Sulastri Sulistiyowati Sulistiyowati Sunardi Sunardi Suprapto, Yossy SUTANTO, FELIX Syahroni Wahyu Iriananda, Syahroni Wahyu Teguh Khristianto Veronica Lusiana Vici Tiara Anjarsari Warto - Wijayanto, Wendhie Tri Wiratno, Amat Wismarini , Th. Dwiati Wiwien Hadi Kurniawati Yayi Suryo Prabandari Yoga Ryan Fatony Yoga Ryan Fatony