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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Evaluation and Research in Education (IJERE) TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Algoritma Jurnal Edukasi dan Penelitian Informatika (JEPIN) Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika JOIN (Jurnal Online Informatika) JOIV : International Journal on Informatics Visualization Jiko (Jurnal Informatika dan komputer) Jurnal Perspektif Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi JISKa (Jurnal Informatika Sunan Kalijaga) JISA (Jurnal Informatika dan Sains) Indonesian Journal of Electrical Engineering and Computer Science Journal of Applied Science, Engineering, Technology, and Education Journal of Applied Data Sciences Jurnal Algoritma Gunung Djati Conference Series Intellect : Indonesian Journal of Learning and Technological Innovation Jurnal Informatika Polinema (JIP) Khazanah Pendidikan Islam International Journal of Islamic Khazanah Khazanah Journal of Religion and Technology
Maylawati, Dian Sa'adillah
Department Of Informatics, UIN Sunan Gunung Djati Bandung
Religion Agriculture, Biological Sciences & Forestry Arts Humanities 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 Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Social Sciences Other

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AUTOMATIC ABSTRACTIVE SUMMARIZATION OF CURRICULUM VITAE USING S-BERT AND T5 Herdiyanto, Reza Fahlevi; Maylawati, Dian Sa'adillah; Lukman, Nur
JIKO (Jurnal Informatika dan Komputer) Vol 8, No 2 (2025)
Publisher : Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v8i2.10019

Abstract

The rapid advancement of technological disruption has catalyzed significant innovations in human resource management, particularly through the widespread adoption of automated applicant screening systems such as Applicant Tracking Systems (ATS). However, these systems often fail to identify potential candidates due to poorly formatted Curriculum Vitae (CV) or missing important keywords, resulting in many applicants being eliminated in the early stages of selection. This research aims to develop an automatic CV summarization system by utilizing Natural Language Processing (NLP) technology. This research uses a combination of Sentence-BERT (SBERT) algorithm for information extraction and Text-to-Text Transfer Transformer (T5) for text generation. The K-Fold Cross Validation method with k = 3 was used in the model performance evaluation, in accordance with the limited computing resources. Experimental results show that the SBERT model is able to extract important information with high accuracy (F1-score of 0.8866), while the T5 model is able to generate informative summaries with a ROUGE-1 score of 0.8680. The combination of SBERT in producing important information extraction from CV and T5 that produces an abstractive summary shows good results with ROUGE-1 scores of 0.5497, ROUGE-2 of 0.3537, and ROUGE-L of 0.4334. This system is able to produce CV summaries that make it easier for companies to select job applicants according to the criteria and increase the chances of applicants to pass the initial selection stage
Implementasi Algoritma Cheapest Insertion Heuristic (CIH) dalam Penyelesaian Travelling Salesman Problem (TSP) Utomo, Rio Guntur; Maylawati, Dian Sa’adillah; Alam, Cecep Nurul
JOIN (Jurnal Online Informatika) Vol 3 No 1 (2018)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v3i1.218

Abstract

Traveling salesman problem (TSP) is the problem of a salesman to visit the city of each city connected to each other and there is the weight of travel between the cities so as to form a complete weighted graph. Departing from a certain initial city, a salesman had to visit (n-1) another city exactly once and return on the initial city of departure. The purpose of TSP is to find the route of all cities with minimum total weight.Many algorithms have been found to solve the TSP, one of which is the Cheapest Insertion Heuristic (CIH) algorithm in the process of inserting weighted steps obtained from the equation c (i, k, j) = d (i, k) + d (k, j) - d (i, j). This algorithm provides different travel routes depending on the order of insertion of cities on the subtour in question.In this final project, the writer took the problem of distribution route of mineral water of al-ma'some 240 ml cup type, with vehicle capacity to meet 1200 carton and have different customer / agent demand that is the distance of depot and agent far from each other, distribution costs.
Deteksi Generatif Teks pada Penilaian Otomatis Tes Esai Berbahasa Indonesia Menggunakan IndoBERT Pitriani, Pitriani; Maylawati, Dian Sa’adillah; Gerhana, Yana Aditia
JEPIN (Jurnal Edukasi dan Penelitian Informatika) Vol 11, No 2 (2025): Volume 11 No 2
Publisher : Program Studi Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/jp.v11i2.93221

Abstract

Hadirnya model generative artificial intelligence (GenAI) membawa tantangan baru dalam dunia pendidikan, khususnya terkait integritas. Salah satu isu yang mencuat adalah potensi penggunaan teks yang dihasil GenAI dalam jawaban pada proses penilaian pembelajaran peserta didik. Oleh karena itu, penelitian ini bertujuan untuk mendeteksi generatif teks hasil perangkat AI pada penilaian otomatis evaluasi pembelajaran dalam bentuk esai dengan bahasa Indonesia. Metode penelitian yang digunakan mengadaptasi model pre-trained Indonesia Bidirectional Encoder Representations from Transformers (IndoBERT). IndoBERT digunakan untuk deteksi generatif teks dengan AI melalui fine-tuning dan penilaian esai otomatis dengan representasi embedding dan cosine similarity dengan mempertimbangkan hasil deteksi GenAI. Hasil eksperimen menunjukkan fine-tuning pada model pre-trained IndoBERT berhasil mencapai akurasi sebesar 93.91% dengan nilai validation loss sebesar sebesar 0.1895. Sementara itu, pada tahap integrasi model deteksi teks GenAI ke dalam penilaian otomatis menunjukkan bahwa deteksi teks GenAI dapat mempengaruhi nilai akhir, khususnya pada jawaban yang memiliki similaritas tinggi dengan kunci jawaban namun terindikasi AI.
Prediction of the COVID-19 Vaccination Target Achievement with Exponential Regression Tju, Teja Endra Eng; Maylawati, Dian Sa’adillah; Munawar, Ghifari; Utomo, Suharjanto
JISA(Jurnal Informatika dan Sains) Vol 4, No 2 (2021): JISA(Jurnal Informatika dan Sains)
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/jisa.v4i2.1051

Abstract

The achievement of the national COVID-19 vaccination target in Indonesia is often reported to be uncertain with various existing obstacles. Prediction with exponential regression modeling is done by adopting part of the SKKNI Data Science with the stages of Data Understanding, Data Preparation, Modeling, Model Evaluation. The vaccination dataset from the Ministry of Health of the Republic of Indonesia for the period from January 13, 2021 to October 10, 2021, was randomly separated into training data of 0.8 parts and testing data of 0.2 parts. The optimal parameters of the exponential function are found using the scipy.optimize library in IPython. The model obtained was evaluated using MAE, RMSE, and R-Squared metrics on normalized training data, training data, test data, and recent data for seven days from 11 to 17 October 2021. The prediction results show that the vaccination target will be achieved 100 percent on January 18, 2022, while on December 31, 2021, only 80 percent will be achieved. From the recent data, it appears that more acceleration is needed, especially if it is desired to be achieved in December 2021 as determined by President Joko Widodo, there will be a shortfall of 20 percent based on the prediction results. 
Real-time object detection to classify export quality of mangosteen using variants of you only look once version 8 Maylawati, Dian Sa'adillah; Fuadi, Mi’raj; Yniarto, Kurniawan; AP, Yuhendra; Nugraha, Rizky Rahmat; Harahap, Akbar Hidayatullah; Wahana, Agung
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i1.pp116-128

Abstract

Mangosteen is one of the leading export commodities from Indonesia. Despite its great economic potential, only about 25% of Indonesian mangosteens meet export standards, mainly due to visual defects such as yellow sap and spots on the skin of the fruit. The process of sorting export worthy mangosteens has been done manually, which tends to be time consuming and inconsistent. Therefore, this study aims to utilize artificial intelligence technology in building a real-time image recognition model to improve the efficiency and accuracy of the export-quality mangosteen sorting process. This study uses you only look once version 8 (YOLOv8) as an image recognition model with YOLOv8 variants, including nano, small, medium, large, and extra large variants. The results of the study using 4,014 primary and 255 secondary data of mangosteen, the highest performance is reached by YOLOv8 medium 82% of accuracy, 0.856 of mean average precision (mAP)50, and 0.616 of mAP50-95. This result is obtained from 70% training, 20% validation, and 10% testing data with epoch stop 85. These results indicate that the model can provide good performance in mangosteen export quality classification. This research contributes to the fields of agricultural technology and artificial intelligence by offering an innovative solution to a practical problem, enhancing efficiency, accuracy, and scalability in export-quality mangosteen sorting.
In-depth Sentiment Analysis of The Independent Campus Program in Islamic Higher Education using Abstractive Summarization Dian Sa'adillah Maylawati; Muhammad Indra Nurardy Saputra; Umar Syahrifudin; Aldi Fahluzi Muharam
Khazanah Pendidikan Islam Vol. 6 No. 3 (2024): Khazanah Pendidikan Islam
Publisher : UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/kpi.v6i3.44500

Abstract

The implementation of the Merdeka Belajar Kampus Merdeka (MBKM) policy in Islamic Higher Education (PTKI) represents a significant shift in Indonesia’s education system. This study evaluates the impact of MBKM on PTKI institutions using sentiment analysis and automatic text summarization. By analyzing 2,416 tweets (2020-2023) from PTKI academics, this study highlights perceptions, challenges, and policy implications. Results indicate that 82.76% of tweets expressed positive sentiment, emphasizing the benefits of MBKM in curriculum flexibility and industry collaboration. However, 17.24% of tweets highlighted challenges, including policy mismanagement, unclear implementation, and student stress. To enhance policy effectiveness, this study recommends stronger institutional support, clearer policy guidelines, and enhanced digital infrastructure to ensure MBKM benefits all PTKI students equitably.
Society's Perspectives on Contemporary Islamic Law in Indonesia through Social Media Analysis Technology: A Preliminary Study Dian Sa'adillah Maylawati; Siah Khosyi'ah; Achmad Kholiq
International Journal of Islamic Khazanah Vol. 12 No. 1 (2022): IJIK
Publisher : UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/ijik.v12i1.15865

Abstract

The social, cultural, and technological developments of society are unavoidable. This has an impact on the development of Islamic Law, which keeps all Muslim activities in the right corridor. Contemporary Islamic law, known as Contemporary Islamic Law, has also developed to answer new societal problems. Various views on Contemporary Islamic Law in solving multiple issues certainly reap various responses from the community and scholars. These views are often conveyed through social media such as Youtube, Instagram, Facebook, and Twitter. Therefore, this article aims to discuss a preliminary study of text analysis techniques used to find out the views of the community and Ulama on Contemporary Islamic Law issues computationally and automatically. This initial study reviews the methods and techniques that will be used, namely the Indonesian National Work Competency Standards (SKKNI) methodology for data science. This study will also use a sentiment analysis approach, topic modeling, and pattern analysis to find out people's views on issues of Contemporary Islamic Law through social media. The algorithm used for sentiment analysis is the Multinomial Naïve Bayes Classifier (MNBC), for topic modeling is Latent Dirichlet Allocation (LDA), while for pattern analysis using the Prefix-projected Sequential Pattern Mining (PrefixSpan) algorithm. The model generated from sentiment analysis, topic modeling, and pattern analysis will be evaluated by measuring the confusion matrix, coherence value, and silhouette coefficient value. In addition, analysis and interpretation of the model results will be carried out in-depth qualitatively by involving the views and thoughts of Islamic Law experts.
Comparison of the Fisher-Yates Shuffle and the Linear Congruent Algorithm for Randomizing Questions in Nahwu Learning Multimedia Ukan Saokani; Mohamad Irfan; Dian Sa'adillah Maylawati; Rachmat Jaenal Abidin; Ichsan Taufik; Riyan Naufal Hay's
Khazanah Journal of Religion and Technology Vol. 1 No. 1 (2023): June
Publisher : Asosiasi Khazanah Cendekia

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

Abstract

Nahwu Quiz is a basic Arabic learning application that can be played by the public over the age of 12 years. In the question practice menu, there are questions and 4 multiple choice questions. The user only needs to choose one of the multiple choices that the user thinks is correct/matches the question at hand. In one game, there are 5 questions. After answering all these questions, you will immediately see the score. The purpose of developing this application apart from being a medium of entertainment as well as a medium of learning and memory training for game users (users). To make this Nahwu Quiz application, the authors use the Fisher Yates Shuffle (FYS) algorithm which is used to perform the randomization function in multiple choice and the Linear Congruent Method (LCM) algorithm as a comparison. White box and black box testing were applied to see the feasibility of the program and to obtain efficiency in the comparison of randomization methods. The results of white box and black box testing on the application show that the application is feasible. with reference to the white box test results that the FYS algorithm and the LCM have the same complexity as the result of cyclomatic complexity = 2.
Data-Driven Insights of the Ecotheology Implementation at Islamic Schools in Indonesia using Machine Learning Dian Sa'adillah Maylawati; Cepy Slamet; Muhammad Khalifa Umana; Akhmad Ridlo Rifa'i; Rohmat Mulyana; Muhammad Ali Ramdhani; Syafi’i Syafi’i
Journal of Applied Science, Engineering, Technology, and Education Vol. 8 No. 1 (2026)
Publisher : PT Mattawang Mediatama Solution

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

Abstract

Ecotheology is an integration of religious values towards awareness of environmental preservation. Indonesia’s Ministry of Religious Affairs has identified ecotheology as a strategic program, including Islamic school students. Therefore, this study aims to reveal the understanding, implementation, challenges, and opportunities of ecotheology in Islamic schools. This research applies data science and machine learning algorithms to analyze a large student dataset, 22,933 data from 32 provinces, with a 41-question validated questionnaire (Cronbach’s Alpha = 0.765, Kappa = 0.791). This research uses K-Means and PCA for clustering to group students by ecotheology awareness and implementation, Association Rules with Apriori algorithm to identify knowledge sources, obstacles, and program linkages, classification using ensemble learning with CatBoost as the best model with 98.71% accuracy, and sentiment analysis using RoBERTa-based Indonesian model on open responses. This research found that students’ understanding of ecotheology is high, with most learning from teachers and others gaining knowledge from social media and books, while implementation remains moderate due to limited programs, policies, and subject integration. In accordance with student’s understanding, the sentiment analysis revealed neutral tones in suggestions but mostly positive expectations, with students desiring more practical, Quran-linked, and community-based activities.
Co-Authors Achmad Kholiq Adi Putra Andriyandi Agung Wahana Ahmad Fathonih, Ahmad Akhmad Ridlo Rifa'i Al-Amin, Muhammad Insan Aldi Fahluzi Muharam Ali, Hapid Barzan Faizin Cecep Nurul Alam Cecep Nurul Alam, Cecep Nurul Cepy Slamet Cepy Slamet Diena Rauda Ramdania Enjang AS, Enjang Fatonah, Fany Risti Fauziah Binti Kasmin Fauziah Binti Kasmin Fitri, Susanti Ainul Ghifari Munawar Hamdan Sugilar Harahap, Akbar Hidayatullah Hartawan, Gaduh Herdiyanto, Reza Fahlevi Hilmi Aulawi Ichsan Budiman Ichsan Taufik Imam Fahmi Fadillah Kasmin, Fauziah Kholiq, Achmad Khosyi'ah, Siah Kumar, Yogan Jaya Lillah, M. Rival Ridautal Lillah Marwah Maulana Sidik Melani Nur Mudyawati Mi’raj Fuadi Mohamad Irfan Muhammad Ali Ramdhani Muhammad Ali Ramdhani Muhammad Humam Wahisyam Muhammad Indra Nurardy Saputra Muhammad Insan Al-Amin Muhammad Khalifa Umana Muharam, Aldi Fahluzi Nugraha, Rizky Rahmat Nur Lukman Nurlatifah, Eva Nurrohman, Nurrohman Pitriani, Pitriani Rachmat Jaenal Abidin Rahman, Titik Khawa Abdul Ridwan Setiawan Riki Ahmad Maulana Rinda Cahyana Rio Guntur Utomo Riyan Naufal Hay's Rizkiansyah Dewantara Rizqullah, Naufal Rohmat Mulyana Rohmat Mulyana Sapdi Rully Agung Yudhiantara Saputra, Muhammad Indra Nurardy Septya Egho Pratama Siah Khosyi'ah Siah Khosyi'ah Syafi’i Syafi’i Syahrifudin, Umar Teddy Mantoro Tedi Priatna Teja Endra Eng Tju Ukan Saokani Umar Syahrifudin Utomo, Suharjanto Wahyudin Darmalaksana Wildan Budiawan Zulfikar Wisnu Uriawan, Wisnu Yana Aditia Gerhana, Yana Aditia Yniarto, Kurniawan Yogan Jaya Kumar Yogan Jaya Kumar Yuhendra AP