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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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Implementasi Algoritma Cheapest Insertion Heuristic (CIH) dalam Penyelesaian Travelling Salesman Problem (TSP) Rio Guntur Utomo; Dian Sa’adillah Maylawati; Cecep Nurul Alam
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.
Exploratory data analysis to reveal learning loss condition in Islamic religious education Rohmat Mulyana; Dian Sa'adillah Maylawati
International Journal of Evaluation and Research in Education (IJERE) Vol 13, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijere.v13i1.26344

Abstract

One of the negative impacts of this prolonged distance teaching and learning activity during the pandemic is that students lag in absorbing lessons, known as learning loss. Islamic religious education (IRE) as value education, especially in Indonesia, can also cause learning loss. This study aims to identify possible indications of learning loss experienced by madrasa students in IRE. This study uses data science methods with an exploratory data analysis (EDA) approach. Respondents are students in the sixth grade of Madrasah Ibtidaiyah (MI), the ninth grade of Madrasah Tsanawiyah (MTs), and the twelfth grade of Madrasah Aliyah (MA). The total respondents in this study were 38,326 MI students, 29,350 MTs students, and 13,474 MA students. The results of the EDA found that most madrasas in Indonesia experienced indications of learning loss in IRE subjects during distance learning during the COVID-19 pandemic, both at the MI, MTS, and MA levels. This study found that the learning loss condition is influenced by various states and readiness for distance learning, both the preparedness of students to learn independently, the availability of digital content that is interesting and easy to understand, as well as the availability of facilities and technology for distance learning.
Implementasi Algoritma K-Nearest Neighbor (KNN) untuk Analisis Sentimen Pengguna Aplikasi Tokopedia Lillah, M. Rival Ridautal Lillah; Maylawati, Dian Sa’adillah; Zulfikar, Wildan Budiawan; Uriawan, Wisnu; Wahana, Agung
Intellect : Indonesian Journal of Learning and Technological Innovation Vol. 2 No. 2 (2023): Intellect : Indonesian Journal of Learning and Technological Innovation
Publisher : Yayasan Lembaga Studi Makwa

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

Abstract

A marketplace is a platform where sellers can come together and sell their goods or services to customers without physical meetings. In the past few decades, marketplaces have become the most popular platform for business sellers to sell their products. Becoming the number 1 marketplace in Indonesia with the most visitors on average is the right marketplace in 2023, namely Tokopedia. However, most people are skeptical of products they have never purchased or used. User reviews play an important role in product marketing, especially on Tokopedia. Reviews help potential customers build trust in the products and services offered by the seller. To analyze reviews quickly and precisely, a sentiment analysis process is needed. Natural Processing Language (NLP) and text mining algorithms are used to classify reviews as positive, or negative. One of the methods used is the K-Nearest Neighbor (KNN) algorithm, which is used to classify Tokopedia user reviews in the Play Store and App Store. The dataset consists of 1000 comment data from the Play Store and 1000 data from the App Store. A total of 2000 comments consisting of 2 labels, namely positive and negative for modeling. Meanwhile, for testing, there were 885,092 comments from the Play Store and 4000 comments from the App Store. Total 889,092 for unlabeled test data. The prediction results on the app store dataset show that there are 97.0% positive label predictions and only 3.0% negative label predictions. Abstrak Marketplace adalah platform tempat penjual dapat berkumpul dan menjual barang atau jasa mereka kepada pelanggan tanpa pertemuan fisik. Dalam beberapa dekade terakhir, pasar telah menjadi platform paling populer bagi penjual bisnis untuk menjual produk mereka. Menjadi marketplace nomor 1 di Indonesia dengan rata-rata pengunjung terbanyak adalah marketplace yang tepat di tahun 2023 yaitu Tokopedia. Namun, kebanyakan orang skeptis terhadap produk yang belum pernah mereka beli atau gunakan. Ulasan pengguna memegang peran penting dalam pemasaran produk, terutama di Tokopedia. Ulasan membantu calon pelanggan membangun kepercayaan terhadap produk dan layanan yang ditawarkan oleh penjual. Untuk menganalisis ulasan dengan cepat dan tepat, diperlukan proses analisis sentimen. Natural Processing Language (NLP) dan algoritma text mining digunakan untuk mengklasifikasikan ulasan sebagai positif, atau negatif. Salah satu metode yang digunakan adalah algoritma K-Nearest Neighbor (KNN), yang digunakan untuk mengklasifikasikan ulasan pengguna Tokopedia di play store dan app store. Dataset terdiri dari 1000 data komentar dari play store dan 1000 data dari app store. Total 2000 komentar yang terdiri dari 2 label yaitu positif dan negatif untuk pemodelan. Sedangkan untuk pengujian 885.092 komentar dari play store dan 4000 komentar dari app store. Total 889.092 untuk data pengujian yang belum dilabeli. Hasil prediksi pada dataset app store menunjukkan terdapat 97,0% prediksi label positif dan hanya 3,0% prediksi label negatif.
Deep sequential pattern mining for readability enhancement of Indonesian summarization Maylawati, Dian Sa'adillah; Kumar, Yogan Jaya; Kasmin, Fauziah; Ramdhani, Muhammad Ali
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i1.pp782-795

Abstract

In text summarization research, readability is a great issue that must be addressed. Our hypothesis is readability can be accomplished by using text representations that keep the meaning of text documents intact. Therefore, this study aims to combine sequential pattern mining (SPM) in producing a sequence of a word as text representation with unsupervised deep learning to produce an Indonesian text summary called DeepSPM. This research uses PrefixSpan as an SPM algorithm and deep belief network (DBN) as an unsupervised deep learning method. This research uses 18,774 Indonesian news text from IndoSum. The readability aspect is evaluated by recall-oriented understudy for gisting evaluation (ROUGE) as a co-selection-based analysis; Dwiyanto Djoko Pranowo metrics, Gunning fog index (GFI), and Flesch-Kincaid grade level (FKGL) as content-based analysis; and human readability evaluation with two experts. The experiment result shows that DeepSPM yields better than DBN, with the F-measure value of ROUGE-1 enhanced to 0.462, ROUGE-2 is 0.37, and ROUGE-L is 0.41. The significance of ROUGE results also be tested using T-Test. The content-based analysis and human readability evaluation findings are conformable with the findings of co-selection-based analysis that generated summaries are only partially readable or have a medium level of readability aspect.
Bidirectional and Auto-Regressive Transformer (BART) for Indonesian Abstractive Text Summarization Hartawan, Gaduh; Maylawati, Dian Sa'adillah; Uriawan, Wisnu
Jurnal Informatika Polinema Vol. 10 No. 4 (2024): Vol. 10 No. 4 (2024)
Publisher : UPT P2M State Polytechnic of Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33795/jip.v10i4.5242

Abstract

Automatic summarization technology is developing rapidly to reduce reading time and obtain relevant information in Natural Language Processing technology research. There are two main approaches to text summarization: abstractive and extractive. The challenge of abstractive summarization results is higher than abstractive because abstractive summarization produces new and more natural words. Therefore, this research aims to produce abstractive summaries from Indonesian language texts with good readability. This research uses the Bidirectional and Auto-Regressive Transformer (BART) model, an innovative Transformers model combining two leading Transformer architectures, namely the BERT encoder and GPT decoder. The dataset used in this research is Liputan6, with model performance evaluation using ROUGE evaluation. The research results show that BART can produce good abstractive summaries with ROUGE-1, ROUGE-2, and ROUGE-L values of 37.19, 14.03, and 33.85, respectively.
Exploring Classification Algorithms for Detecting Learning Loss in Islamic Religious Education: A Comparative Study Sapdi, Rohmat Mulyana; Maylawati, Dian Sa'adillah; Ramdania, Diena Rauda; Budiman, Ichsan; Al-Amin, Muhammad Insan; Fuadi, Mi'raj
JOIV : International Journal on Informatics Visualization Vol 8, No 2 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.2.1823

Abstract

This study investigates the detection of learning loss in Islamic religious education subjects in Indonesia. Focusing on the effectiveness of multiple classification algorithms, the research assesses learning loss across literacy, numeracy, writing, and science domains. While education traditionally involves knowledge transmission, it also seeks to instill values. Given Indonesia's predominantly Islamic demographic, Islamic Religious Education (IRE) is pivotal in disseminating moral and cultural values, encompassing teachings from the Koran, Hadith, Aqedah, morality, Fiqh, and Islamic history. The study's central aim is to discern learning loss in IRE in Islamic schools, utilizing the Gradient Boosting Classifier as its primary analytical tool. Various classification algorithms, including the Cat Boost Classifier, Light Gradient Boosting Machine, Extreme Gradient Boosting, and others, were tested. The study engaged a sample of 38,326 Islamic Elementary school students, 29,350 Islamic Junior High school students, and 13,474 Islamic High school students across Indonesia. The findings revealed that the Light Gradient Boosting Machine was the most effective model for Islamic Elementary and High school data, while the Cat Boost Classifier excelled for Islamic Junior High school data. These results highlight the extent of learning loss in IRE and offer invaluable perspectives for education stakeholders. Future studies are encouraged to further explore the root causes of this learning loss and devise specific interventions to tackle these issues effectively.
Chatbot Edukasi Pra-Nikah berbasis Telegram Menggunakan Bidirectional Encoder Representations From Transformers (BERT) Fatonah, Fany Risti; Maylawati, Dian Sa'adillah; Nurlatifah, Eva
Jurnal Algoritma Vol 21 No 2 (2024): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.21-2.1657

Abstract

Tingginya angka perceraian dan penurunan minat untuk menikah di Indonesia memunculkan kebutuhan akan pendekatan baru dalam edukasi pranikah. Dengan memanfaatkan teknologi Natural Language Processing, penelitian ini bertujuan untuk mengembangkan mesih chatbot menjadi solusi dalam edukasi pre-nikah yang dengan memberikan informasi efektif dan efisien kepada pasangan calon pengantin secara realtime. Penelitian ini menggunakan model Bidirectional Encoder Representations from Transformers (BERT) dengan chatbot berupa konteks dari website Kementerian Agama dan buku edukasi pernikahan. Model ini diimplementasikan ke dalam chatbot melalui platform Telegram dan pengujiannya menggunakan pengujian Non-Respon-Rate dan metriks BERTScore. Hasil pengujian Non-Respon-Rate menunjukkan akurasi chatbot edukasi pranikah berbasis BERT sebesar 76,92% dengan akurasi tertinggi 92%. Sedangkan pengujian menggunakan BERTScore menunjukkan bahwa chatbot tersebut mencapai nilai precision 86%, recall 83%, dan F1-score 84%.
Polarization of Religious Issues in Indonesia’s Social Media Society and Its Impact on Social Conflict Faizin, Barzan; Fitri, Susanti Ainul; AS, Enjang; Maylawati, Dian Sa'adillah; Rizqullah, Naufal; Ramdhani, Muhammad Ali
Journal of Applied Data Sciences Vol 6, No 1: JANUARY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i1.447

Abstract

In this new era, people use social media to share information and discuss political, social, and religious issues, leading to pros and cons arguments. In Twitter’s hashtags and tweets, religious issues frequently trigger a hot conversation that causes disputes among citizens and even street movements. This study is intended to reveal the religious issues that often trigger polarization among Twitter users and how they influence horizontal conflict in society as what happened during the election period in 2019. This research applied mixed methods with social media analytics to conceal religious issues in Indonesia's social media society. The data collection was done by crawling data from the Indonesian Twitter users’ tweets regarding religious issues hashtags, which is a reference for further analysis. The research findings show that the top eight religious issues widely discussed based on 23,433 Twitter users’ tweets are the hashtags (#) salafi, wahabi, intoleransi (intolerance), taliban, anti-Pancasila, politisasi agama (politicization of religion), politik identitas (identity politics), and radikalisme (radicalism). In social conversation networks, these issues are related to each other and other issues such as political figures, the three presidential candidates, the general election, and the Republic of Indonesia presidential election in 2024. Concerning these issues, Twitter users believe that the issues, positive or negative, do not influence their religious and political stance. However, to a certain extent, they believe that religious issues impact social discourses regarding horizontal conflicts. 44% opinions prove this indicated that the debate over religious matters had little influence on their opinion of these topics, and 64.5% agreed that religious concerns can cause social strife. Finally, it is hoped that further studies will elaborate on how religious issues on Twitter and other social media directly impact social harmony in everyday life.
Enhancing Abstractive Multi-Document Summarization with Bert2Bert Model for Indonesian Language Muharam, Aldi Fahluzi; Gerhana, Yana Aditia; Maylawati, Dian Sa'adillah; Ramdhani, Muhammad Ali; Rahman, Titik Khawa Abdul
JISKA (Jurnal Informatika Sunan Kalijaga) Vol. 10 No. 1 (2025): January 2025
Publisher : UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/jiska.2025.10.1.110-121

Abstract

This study investigates the effectiveness of the proposed Bert2Bert and Bert2Bert+Xtreme models in improving abstract multi-document summarization for Indonesians. This research uses the transformer model to develop the proposed Bert2Bert and Bert2Bert+Xtreme models. This research utilizes the Liputan6 data set, which comprises news data along with summary references spanning 10 years from October 2000 to October 2010, and is commonly used in many automatic text summarization studies. The model evaluation results using ROUGE-1, ROUGE-2, ROUGE-L, and BERTScore indicate that the proposed model exhibits a slight improvement over previous research models, with Bert2Bert performing better than Bert2Bert+Xtreme. Despite the challenges posed by limited reference summaries for Indonesian documents, content-based analysis using readability metrics, including FKGL, GFI, and Dwiyanto Djoko Pranowo, revealed that the summaries produced by Bert2Bert and Bert2Bert+Xtreme are at a moderate readability level, meaning they are suitable for mature readers and align with the news portal’s target audience.
Text Generation untuk Profil Mata Kuliah pada Penilaian Outcome-Based Education Menggunakan Text-to-Text Transfer Transformers Nurrohman, Nurrohman; Maylawati, Dian Sa'adillah; Al-Amin, Muhammad Insan
Jutisi : Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Vol 14, No 1: April 2025
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/jutisi.v14i1.2579

Abstract

The evaluation of Course Learning Outcomes (CPMK) in Outcome-Based Education (OBE) is still conducted manually, making it time-consuming and prone to errors. Additionally, the achievement profile of CPMK is often overlooked. This study aims to automate the generation of course profiles based on CPMK using Text Generation technology. The method employed is Transformers with the T5 (Text-to-Text Transfer Transformer) algorithm. Experiments were conducted using three variants of the T5 model: T5 Base, T5 Base with fine-tuning, and T5 XL, evaluated using the Bilingual Evaluation Understudy (BLEU) and Recall-Oriented Understudy for Gisting Evaluation (ROUGE) metrics. The results show that T5 XL achieved the best performance, with an average BLEU score of 0.592 and a ROUGE-L score of 0.721. T5 Base with fine-tuning recorded a BLEU score of 0.417 and a ROUGE-L score of 0.468, while T5 Base without fine-tuning had a BLEU score of 0.327 and a ROUGE-L score of 0.246. Additionally, more structured prompts yielded better evaluation results. This study demonstrates that T5 XL enhances the efficiency and accuracy of CPMK evaluation in OBE.Keywords: Outcome Based Education; Text Generation; Text-To-Text Transfer Transformers; Penilaian AbstrakEvaluasi capaian pembelajaran mata kuliah (CPMK) dalam Outcome-Based Education (OBE) masih dilakukan secara manual, memakan waktu, dan rentan terhadap kesalahan. Selain itu, profil pencapaian CPMK sering diabaikan. Penelitian ini bertujuan mengotomasi pembuatan profil mata kuliah berbasis CPMK menggunakan teknologi Text Generation. Metode yang digunakan adalah Transformers dengan algoritma T5 (Text-to-Text Transfer Transformers). Eksperimen dilakukan dengan tiga varian model T5: T5 Base, T5 Base dengan fine-tuning, dan T5 XL, dievaluasi menggunakan metrik Bilingual Evaluation Understudy (BLEU) dan Recall-Oriented Understudy for Gisting Evaluation (ROUGE). Hasil menunjukkan T5 XL memiliki performa terbaik dengan BLEU rata-rata 0,592 dan ROUGE-L 0,721. T5 Base dengan fine-tuning mencatat BLEU 0,417 dan ROUGE-L 0,468, sedangkan T5 Base tanpa fine-tuning memiliki BLEU 0,327 dan ROUGE-L 0,246. Selain itu, prompt yang lebih terstruktur menghasilkan evaluasi lebih baik. Penelitian ini membuktikan bahwa T5 XL meningkatkan efisiensi dan akurasi evaluasi CPMK dalam OBE. 
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