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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Telematika : Jurnal Informatika dan Teknologi Informasi Jurnal Profesi Medika: Jurnal Kedokteran dan Kesehatan International Journal of New Media Technology Compiler JOURNAL OF SCIENCE AND APPLIED ENGINEERING IJID (International Journal on Informatics for Development) Informatika : Jurnal Informatika, Manajemen dan Komputer JISKa (Jurnal Informatika Sunan Kalijaga) Cyber Security dan Forensik Digital (CSFD) Jurnal Berdaya Mandiri Indonesian Journal of Data and Science Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) International Journal of Health Science and Technology Indonesian Journal on Data Science Teknomatika: Jurnal Informatika dan Komputer
Muhammad Habibi
Universitas Jenderal Achmad Yani Yogyakarta
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PERBANDINGAN METODE DECISION TREE DAN NAIVE BAYES CLASSIFIER PADA ANALISIS SENTIMEN PENGGUNA LAYANAN PT PERUSAHAAN LISTRIK NEGARA (PLN) ABIYOGA BAGUS MUSTRIYANTO; Muhammad Habibi; Dayat Subekti; Fajar Syahruddin
Jurnal Teknomatika Vol 15 No 2 (2022): TEKNOMATIKA
Publisher : Fakultas Teknik dan Teknologi Informasi, Universitas Jenderal Achmad Yani Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30989/teknomatika.v15i2.1131

Abstract

Background : PLN is a state-owned company that is tasked with supplying electricity to all regions of Indonesia which certainly cannot be separated from the various obstacles experienced, to find out public sentiment on the services that have been provided, an analysis is carried out to determine public sentiment. The results of these sentiments are created in the dashboard using the Flask framework by comparing the Naive Bayes and Decision tree methods. To create a sentiment analysis dashboard for PT. PLN and make a research analysis model using a comparison of the Naive Bayes Classification and Decision tree methods. The method used in this research is Naive Bayes and Decision tree. The data obtained with a total of 40,745 Tweet data taken in the period 1 May 2022 - 4 June 2022 with the keyword "PLN". Making a dashboard that displays the results of the analysis where there is a menu to display the data and each analysis process. The use of 900 training data and 300 testing data resulted in the Naive Bayes method getting an accuracy of 83% on the training data and 80% for the Testing data, while the Decision tree method got an accuracy of 77% on the Training data and 56% on the Testing data. The analysis obtained for the method in this study also shows that the Naive Bayes method is better for classifying large amounts of data than the Decision tree. The sentiment generated by the highest number is negative, with most of the Tweets being complaints about the response to complaints and handling of damage reported by the public.
Metode Hybrid Menggunakan Pendekatan Lexicon Based dan Naive Bayes Classifier Untuk Analisis Sentimen Terkait Jaminan Hari Tua Rizky Fauzi Akbar; Muhammad Habibi; Puji Winar Cahyo; Nafisa Alfi Sa'diya
Jurnal Teknomatika Vol 16 No 2 (2023): TEKNOMATIKA
Publisher : Fakultas Teknik dan Teknologi Informasi, Universitas Jenderal Achmad Yani Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30989/teknomatika.v16i2.1247

Abstract

Badan Penyelenggara Jaminan Sosial (BPJS) Ketenagakerjaan adalah badan aturan publik yang dibuat melalui Undang-Undang No 24 Tahun 2011 Tentang Badan Penyelenggaran Jaminan Sosial menggunakan tujuan untuk mewujudkan terselenggaranya pemberian jaminan terpenuhinya kebutuhan dasar yang layak bagi setiap peserta atau anggota keluarganya. Dalam pelaksanaannya terdapat informasi yang tersebar khususnya pada tweet di Twitter mengenai keputusan Kementrian Kesehatan yaitu mengenai Jaminan Hari Tua (JHT) yang hanya bisa dicairkan/diambil setelah peserta (BPJS) Ketenagakerjaan menginjak usia 56 tahun, menyebabkan adanya pro dan kontra yang ada dikalangan masyarakat. Berdasarkan tweet-tweet pada Twitter yang belum dianalisis maka perlu di analisis secara mendalam untuk mendapatkan informasi yang sesuai berdasarkan opini netizen. Berdasarkan hasil penelitian ini diperoleh nilai akurasi data testing sebesar 92% untuk metode Lexicon Based dan 95% untuk data testing pada metode Naïve Bayes Classifier lalu untuk data training Naïve Bayes Classifier mendapatkan akurasi 82%. Penelitian ini mendapatkan kesimpulan bahwa jaminan hari tua (JHT) pada (BPJS) Ketenagakerjaan mendapat sentimen negatif dari netizen yang banyak membahas mengenai penolakan peraturan baru dimana jaminan hari tua (JHT) pada (BPJS) Ketenagakerjaan, hanya bisa dicairkan atau diambil ketika peserta BPJS Ketenagakerjaan menginjak usia 56 tahun.
Tweet Analysis of Mental Illness Using K-Means Clustering and Support Vector Machine Kartikadyota Kusumaningtyas; Muhammad Habibi; Irmma Dwijayanti; Retno Sumiyarini
Telematika Vol 20, No 3 (2023): Edisi Oktober 2023
Publisher : Jurusan Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v20i3.9820

Abstract

Purpose: Social media, particularly Twitter, provides a venue for individuals to share their thoughts. The public's perception of mental illnesses is often debated on Twitter. So yet, no evaluation of community tweets connected to data on mental health conditions has been performed. The purpose of this study is to examine tweets linked to mental illnesses in Indonesia in order to identify the themes of conversation and the polarity trends of these tweets.Design/methodology/approach: To address this issue, the K-Means Clustering algorithm is utilized to aggregate tweet data that is used to find themes of conversation. The emotion polarity value of each cluster result was then determined using the Support Vector Machine (SVM) approach.Findings/results: This study generated five topic clusters based on tweets about mental illness. While sentiment analysis revealed that all clusters had more negative sentiment classes than positive. Cluster 4 and Cluster 5 had the highest number of negative sentiment values. These clusters emphasize the necessity of consulting with psychiatrists and psychologists if people have mental health disorders, as well as financing for mental health disorder treatment through BPJS Kesehatan services.Originality/value/state of the art: The analysis was done in two stages: data grouping to find themes of conversation using K-Means clustering and SVM to look for positive and negative polarity values associated to twitter data about mental illness.
Innovation of M-Health-Based Prosa-Hi Application for Early Detection of Child Growth and Development Sunarsih, Tri; Astuti, Endah Puji; Purnamaningsih, Nur'Aini; Suwarno; Syah, Muhammad Erwan; Shanti, Elvika Fit Ari; Habibi, Muhammad; Kharisma; Rukmi, Dwi Kartika
Jurnal Profesi Medika : Jurnal Kedokteran dan Kesehatan Vol 17 No 2 (2023): Jurnal Profesi Medika : Jurnal Kedokteran dan Kesehatan
Publisher : Fakultas Kedokteran UPN Veteran Jakarta Kerja Sama KNPT

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33533/jpm.v17i2.6974

Abstract

The development of technology has advanced digital and intelligent transformation, including in the child health. This study was conducted to implement the PROSA-HI Application to detect child growth early. The research method uses the sequential mix methods. The PROSA-HI Application will be implemented in Nogotirto Village from August 2022 to November 2022. The data collection techniques in this study include Observation, Interviews, and questionnaires with mothers with toddlers using the User Acceptance Test (UAT) questionnaire of 291 mothers under five. Analysis by univariate analysis. Testing the PROSA-HI Application with a user acceptance test showed an average of 89.0%, so it can be concluded that the usability rate of the PROSA-HI application system based on user perception is considered feasible to implement. The PROSA-HI Application can effectively monitor children's health, growth, and development, positively impacting parents and health workers.
Tweets Classification of Mental Health Disorder in Indonesia Using LDA and Cosine Similarity Dwijayanti, Irmma; Habibi, Muhammad; Kusumaningtyas, Kartikadyota; Riyadi, Sujono
Telematika Vol 21, No 1 (2024): Edisi Februari 2024
Publisher : Jurusan Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v21i1.10725

Abstract

Purpose: Twitter related to mental health has great potential as a medium to provide important information to the public and health organizations on a large scale, but an evaluation of tweet data related to mental health disorders has not been carried out. This study aims to classify tweet data to determine the most common mental health disorders in Indonesia based on the symptoms experienced.Methodology: The classification process is carried out using cosine similarity calculations between tweets data and keywords which are compiled based on theoretical studies and optimization of the LDA topic modeling results.Findings/result:The classification results show that the most discussed issues on Twitter are depression, bipolar, schizophrenia, dementia, and PTSD. Based on these results it can be interpreted that the level of prevalence and public attention to depressive diorders is quite high compared to other disorders. From the results of the classification, it is also possible to identify the most discussed symptoms throughthe emergence of keywords from each category.Originality: Classification is calculated based on the cosine similarity between tweets and keywords compiled from human judgement and enriched using the results of LDA topic modeling to improve classification performance
Sentiment Analysis Related National Social Security Agency for Employment in Indonesia: Hybrid Method Using Lexicon Based and Naive Bayes Classifier Approaches Rizky Fauzi Akbar; Habibi, Muhammad
INDONESIAN JOURNAL ON DATA SCIENCE Vol 1 No 1 (2023): 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.v1i1.896

Abstract

The National Social Security Agency (BPJS) for Employment is the Social Security Administering Agency with the goal of ensuring that each participant or member of the family receives adequate necessities. In its implementation, there is information that is spread, particularly on Twitter, regarding the Ministry of Health's decision, namely regarding Old Age Security (JHT), which can only be distributed/taken after the participant turns 56 years old, causing both pros and cons among the public. Based on unanalyzed tweets on Twitter, it is necessary to do extensive research to collect relevant information based on netizens' viewpoints. This research describes sentiment analysis of tweets from Twitter using the terms JHT, BPJSTK, and BPJS, which yield 4154 data tweets. We employ two approaches in this study: Lexicon Based and Nave Bayes Classifier. According to this study, the accuracy of the testing data is 92% for the Lexicon Based and 95% for the Nave Bayes Classifier. This study concluded that the JHT at BPJS Employment received unfavorable attitudes and negative reactions among users who addressed the rejection of new restrictions where JHT, could only be dispensed or taken when participants at BPJS Employment were 56 years old.
Pemetaan Opini Publik Menggunakan Data Mining: Studi Kasus Naturalisasi Pemain Sepak Bola dengan K-Means dan Naive Bayes Classifier Tegar Agustian; Fresia Nandela, Emilia; A. Sinay, Stani; Habibi, Muhammad
INDONESIAN JOURNAL ON DATA SCIENCE Vol 2 No 1 (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

Abstract

Naturalisasi merupakan salah satu proses yang dilakukan oleh warga asing agar menjadi Warga Negara Indonesia (WNI) yang sah di mata hukum. Saat ini Timnas Indonesia memiliki beberapa pemain naturalisasi . Beberapa kalangan menyambut positif kehadiran mereka, melihatnya sebagai langkah strategis untuk meningkatkan kualitas dan daya saing tim. Namun, ada pula yang merasa skeptis dan meragukan keberlanjutan dukungan terhadap pemain lokal. Data yang diambil dari 3584 komentar YouTube melalui YouTube Data API mencerminkan keragaman opini yang dapat memberikan gambaran lebih mendalam tentang dinamika pandangan publik. Penelitian ini penting dalam konteks pemahaman pandangan masyarakat terhadap naturalisasi pemain sepak bola Timnas. Dengan menggunakan teknik Data Mining, terutama K-Means Clustering dan Naive Bayes Classifier, penelitian ini memberikan wawasan mendalam tentang kelompok-kelompok masyarakat dengan perspektif serupa atau berbeda terkait isu tersebut. Hasil dari proses K-Means Clustering digunakan sebagai label awal untuk melatih model Naive Bayes Classifier. Evaluasi kinerja model dilakukan menggunakan confusion matrix, yang menghasilkan akurasi sebesar 93,17% dan error rate sebesar 6,83%. Proses ini dilakukan terhadap dataset komentar YouTube yang telah diberi label melalui K-Means Clustering. Hasil klasifikasi menggunakan metode Naive Bayes menunjukan bahwa 3328 data komentar setuju dengan adanya naturalisasi pemain dan 256 data komentar tidak setuju.
Metode Latent Dirichlet Allocation Untuk Menentukan Topik Pada Review Drama Korea Alfun Roehatul Jannah; Kristi, Ria; Muhammad Habibi
INDONESIAN JOURNAL ON DATA SCIENCE Vol 2 No 1 (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.v2i1.1345

Abstract

The Hallyu Wave, involving the spread of South Korean culture and popular media, has rapidly grown over the past two decades. In addition to entertainment industries such as K-pop and K-drama, this phenomenon has also extended into the food and K-beauty sectors. Korean dramas, as the core of Hallyu, have become a global phenomenon with a continuously expanding fan base worldwide. A global survey in 2022 indicated that 36 percent of respondents in 26 countries considered Korean dramas very popular in their respective countries. In Indonesia, Korean films and dramas remain favorites, with 72 percent of streaming audiences choosing them on OTT services throughout 2022. Viu dominates as the most popular Korean drama streaming platform with 57 percent usage, followed by Netflix, Telegram, and WeTv. This research focuses on the analysis of Korean drama review data from 2015 to 2023 using the Latent Dirichlet Allocation (LDA) method. The goal is to provide a deep understanding of critical aspects such as acting, storyline, and cinematography. With LDA, this research aims to identify topics related to these elements, offering specific insights into audience preferences. From the conducted research, 10 ideal topics emerged out of 20 existing topics to ensure topic consistency using topic coherence. From the topic coherence results for these 20 topics, it can be concluded that the overall topic score for topic 10 is 0.527, providing ideal results for topic modeling in accordance with the rules.
ANALISIS PROYEKSI KEBUTUHAN TENAGA KERJA BERDASARKAN SKILLS YANG DIBUTUHKAN MENGGUNAKAN ALGORITMA NAIVE BAYES CLASSIFIER Nur Azizah Firdausa; Rifanny Br Girsang, Ribka; Oktaviana, Dela; Wahyuningsiam, Astr; Habibi, Muhammad
INDONESIAN JOURNAL ON DATA SCIENCE Vol 2 No 1 (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.v2i1.1346

Abstract

In August 2023, Indonesia faced an unemployment rate of 7.86 million people, although there is no denying that the percentage of unemployment has decreased from the previous year. The data is categorized into four groups, namely unemployment involves those who are looking for work, trying to set up a business having trouble landing a job, and even those who have worked but have not started. The Covid-19 pandemic changed the paradigm of work to remote, but the need for job information remains key. Labor demand projections provide long-term insights into promising sectors and fields, guiding job seekers to develop skills according to labor market trends. This research was conducted using naive bayes classification, which is a text classification method that relies on the likelihood of keywords to compare training and testing documents. This classification method is expected to help reduce unemployment rates and align individual skills with industry needs, contributing to education and training policies to make smart career decisions in the digital era.
Predictive Modeling of Air Quality Levels Using Decision Tree Classification: Insights from Environmental and Demographic Factors Iwan Sudipa, I Gede; Habibi, Muhammad; Jullev Atmadji, Ery Setiyawan; Arfiani, Ika
Indonesian Journal of Data and Science Vol. 5 No. 3 (2024): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v5i3.201

Abstract

Air pollution poses a significant global challenge, adversely impacting public health and environmental sustainability. Understanding the factors influencing air quality is essential for developing effective mitigation strategies. This study aims to analyse key environmental and demographic factors, such as PM2.5 concentration, population density, and proximity to industrial areas, to predict air quality levels using a Decision Tree model. The dataset, comprising 5000 samples, was pre-processed by encoding the target variable and applying Z-score normalization to numerical features. The model was trained on 80% of the data and evaluated on the remaining 20%, achieving an accuracy of 93%. Evaluation metrics, including a classification report and confusion matrix, demonstrated the model's effectiveness in distinguishing between four air quality categories: Good, Moderate, Poor, and Hazardous. PM2.5 emerged as the most critical predictor, followed by demographic and industrial factors. These findings underscore the potential of machine learning models in providing actionable insights for air quality management. The results contribute to public policy by highlighting the need for targeted interventions in high-risk areas and the importance of incorporating environmental data into urban planning. Future work should focus on expanding the feature set and exploring ensemble techniques to further enhance predictive accuracy and robustness.
Co-Authors A. Sinay, Stani ABIYOGA BAGUS MUSTRIYANTO Adri Priadana Alfirna Rizqi Lahitani Alfun Roehatul Jannah Alfun Roehatul Jannah Aris Wahyu Murdiyanto dayat subekti Dedy Hariyadi Elvika Fit Ari Shanti, Elvika Fit Ari Endah Puji Astuti, Endah Puji Fajar Syahruddin Fresia Nandela, Emilia I Gede Iwan Sudipa Ika Arfiani Iqbal Hadi Subekti Irmma Dwijayanti Jenny Meilila Azani Cahya Permata Jullev Atmadji, Ery Setiyawan Kharisma Kristi, Ria Kusumaningtyas, Kartikadyota Lahitani, Alfirna Rizqi M. Wahyu Indriyanto Muhammad Erwan Syah Muhammad Rifqi Ma'arif Nafisa Alfi Sa'diya Nasron, Ulil Amri Netania Indi Kusumaningtyas Nur Azizah Firdausa Oktaviana, Dela Pangky Putra Aziztiya Puji Winar Cahyo Purnamaningsih, Nur’Aini Purwatiningsih, Agustina Ramses Caniago Rifanny Br Girsang, Ribka Rifqi Ma’arif, Muhammad Risky Setyadi Putra Rizky Fauzi Akbar Rizky Fauzi Akbar Rizqi Lahitani, Alfirna Rukmi, Dwi Kartika Saputra, Andika Bayu Sujono Riyadi Sumiyarini, Retno Suwarno Tegar Agustian Tri Sunarsih Ulil Amri Nasron Wahyuningsiam, Astr