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Contact Name
Ulfi Saidata Aesyi
Contact Email
ijds.unjaya@gmail.com
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+6285643086972
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ijds.unjaya@gmail.com
Editorial Address
Jl. Siliwangi, Ringroad Barat, Banyuraden, Gamping, Sleman Daerah Istimewa Yogyakarta 55293
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Kab. sleman,
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INDONESIA
Indonesian Journal on Data Science
Published by Universitas Jenderal Achmad Yani Yogyakarta
ISSN : 29877423     EISSN : 29877423     DOI : 10.30989
Core Subject : Science,
Computer Science & IT
Indonesian Journal of Data Science (IJDS) adalah Jurnal ilmiah yang memuat hasil penelitian pada ranah data science (Ilmu Data). Cangkupan jurnal meliputi: 1. Big Data 2. Machine Learning 3. Data Mining 4. Deep Learning 5. Artificial Intelligence
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 5 Documents
 1 
Search results for , issue "Vol. 2 No. 2 (2024): Indonesian Journal On Data Science" : 5 Documents clear
ANALLISIS PERBANDINGAN METODE FUZZY MAMDANI DAN FUZZY TSUKAMOTO DALAM MENGUKUR KEPUASAN PENDUDUK TERHADAP KINERJA PEGAWAI DI NEGERI ALLANG Upuy, Doms; Saidu , Rusnian Isfahami; Salamena , Gieska Nataly; Juma , Arman; Lopumeten, Jesica; Palembang, Citra Fathia
INDONESIAN JOURNAL ON DATA SCIENCE Vol. 2 No. 2 (2024): Indonesian Journal On Data Science
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat Universitas Achmad Yani Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30989/ijds.v2i2.1384

Abstract

The aim of this research is to evaluate the level of population satisfaction with employee performance in Allaug State. The Fuzzy Mamdani and Fuzzy Tsukamoto methods are used to process qualitative and quantitative data. This study involved a survey of 100 Allaug State residents, using a questionnaire covering various aspects of public service. The research results show that the Fuzzy Tsukamoto method produces a higher level of population satisfaction compared to the Fuzzy Mamdani method. Further analysis reveals that factors such as service speed, employee friendliness, and procedural efficiency have a significant influence on satisfaction levels. This research also identifies areas that need improvement in public services in Allaug State. The implications of these findings are discussed in the context of improving government service quality and community welfare.
PREDICTING STUDENT GRADUATION USING THE FUZZY TSUKAMOTO METHOD IN COMPUTER SCIENCE STUDY PROGRAM STUDENTS CLASS OF 2022 Upuy, Doms; Suhardin, Askin; Sapri, Ismu Iqbal
INDONESIAN JOURNAL ON DATA SCIENCE Vol. 2 No. 2 (2024): Indonesian Journal On Data Science
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat Universitas Achmad Yani Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30989/ijds.v2i2.1395

Abstract

This research aims to help optimize resources by designing a system that can be used to help predict student graduation at Pattimura University. The system method used is the Tsukamoto fuzzy method. Tsukamoto's method is an extension of monotonic reasoning. In the Tsukamoto method, each consequence of a rule in the form of IF-THEN must be represented by a fuzzy set with a monotonic membership function. As a result, the inference output from each rule is given firmly (crisp) based on the ?-predicate (fire strength). The final result is obtained using a weighted average. The result of this research is a student graduation prediction system to optimize good results and avoid errors that occur when predicting student graduation.
Penerapan Algoritma Support Vector Machine (SVM) untuk Deteksi Penyakit Mata Katarak: Algoritma Support Vector Machine (SVM) untuk Deteksi Penyakit Mata Katarak Supit, Yonal
INDONESIAN JOURNAL ON DATA SCIENCE Vol. 2 No. 2 (2024): Indonesian Journal On Data Science
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat Universitas Achmad Yani Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30989/ijds.v2i2.1449

Abstract

Eye diseases are one of the health disorders that can have serious consequences if not diagnosed early. In an effort to improve the accuracy and efficiency of eye disease detection, the Support Vector Machine (SVM) method is used for classifying eye diseases based on image datasets or related numerical data. This research aims to implement SVM as a classification algorithm, utilizing features extracted from eye images or relevant medical data. The research process includes data collection, preprocessing, feature extraction, SVM model training, and model performance evaluation using accuracy, precision, recall, and F1-score metrics. By applying k-fold cross-validation techniques, the model is tested to avoid overfitting and ensure good generalization. The results of the study show that the SVM method can provide accurate classification results and can be used as an effective tool for diagnosing eye diseases.
ANALISIS PERSEPSI PUBLIK DI MEDIA SOSIAL TERHADAP PRODUK HALAL DI INDONESIA MENGGUNAKAN TEXT MINING Maarif, Muhammad Rifqi; Salim, D Jayus Nor
INDONESIAN JOURNAL ON DATA SCIENCE Vol. 2 No. 2 (2024): Indonesian Journal On Data Science
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat Universitas Achmad Yani Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30989/ijds.v2i2.1487

Abstract

The assurance of the halal status of a product is crucial for Muslim consumers, especially in Indonesia, the country with the largest Muslim population in the world. The government, through the Ministry of Religious Affairs, has implemented halal certification to ensure the validity of products. However, the implementation of the halal assurance system remains controversial, with some viewing it as an effort for government profit, particularly by the Ministry of Religious Affairs through the Indonesian Council of Ulama (MUI). Negative sentiments from opposing groups have the potential to escalate and influence other individuals or communities if not properly addressed. One preventive measure is to promote education and awareness of halal products through digital media, especially social media. Given the widespread use of social media, particularly among millennials, it can serve as an effective and cost-efficient platform for education and promotion. Educational efforts via social media require specific strategies to ensure the optimal dissemination of information as intended. Understanding the patterns and characteristics of content as well as the socio-cultural context of social media users is essential. This understanding can be achieved through a comprehensive analysis of the diverse and abundant user-generated content on social media platforms. Therefore, this study proposes a text mining-based method to develop a framework for understanding the characteristics and dynamics of public discourse on halal products on social media.
Sistem Chabot Layanan Informasi Mahasiswa Menggunakan Algoritma Long Short-Term Memory Arumsari, Dewi; kharisma; Aesyi, Ulfi Saidata
INDONESIAN JOURNAL ON DATA SCIENCE Vol. 2 No. 2 (2024): Indonesian Journal On Data Science
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat Universitas Achmad Yani Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30989/ijds.v2i2.1489

Abstract

In the era of globalization and rapid information flow, the demand for efficient and accurate information, especially within academic institutions, is rising. Students often face challenges in accessing educational resources and real-time information, particularly outside official working hours. Existing online information services have limitations in providing continuous access. This research focuses on developing and evaluating a student information service chatbot system at Universitas Jenderal Achmad Yani Yogyakarta (UNJAYA) using the Long Short-Term Memory (LSTM) algorithm. The primary objective is to create a system that delivers real-time, accurate, and efficient information services to students. The Machine Learning Development Cycle (MLDC) is employed in the model development process, including stages such as data collection, processing, model training, evaluation, and implementation. The system's performance is tested using a questionnaire distributed to students, with responses measured on a Likert scale. The results demonstrate a chatbot with a 97.76% accuracy rate, 98.34% precision, and 97.76% recall. The overall system evaluation yielded an average score of 3.87, categorized as good. This research concludes that the LSTM-based chatbot successfully enhances information services at the Faculty of Engineering and Information Technology, providing an innovative solution to meet student needs in real-time

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