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All Journal Journal of Tropical Life Science : International Journal of Theoretical, Experimental, and Applied Life Sciences Jurnal Kesehatan Lingkungan indonesia DEDIKASI JURNAL MAHASISWA Jurnal Online Mahasiswa (JOM) Bidang Perikanan dan Ilmu Kelautan Jurnal Online Mahasiswa (JOM) Bidang Ilmu Hukum Jurnal Ilmiah Mahasiswa FEB Telematika : Jurnal Informatika dan Teknologi Informasi Jurnal Al-Tadzkiyyah JURNAL PENGABDIAN KEPADA MASYARAKAT Cosmogov: Jurnal Ilmu Pemerintahan Journal of Governance and Public Policy JURNAL KESEHATAN LINGKUNGAN: Jurnal dan Aplikasi Teknik Kesehatan Lingkungan Otoritas : Jurnal Ilmu Pemerintahan MODELING: Jurnal Program Studi PGMI Pendas : Jurnah Ilmiah Pendidikan Dasar Politik Indonesia: Indonesian Political Science Review Compiler Al-Hikmah Jurnal Basicedu IJID (International Journal on Informatics for Development) Jurnal Pengabdian Magister Pendidikan IPA Jurnal Surya Kencana Satu : Dinamika Masalah Hukum dan Keadilan Riyadhoh : Jurnal Pendidikan Olahraga Abdimas Universal Jurnal Socius: Journal of Sociology Research and Education Media Syari'ah: Wahana Kajian Hukum Islam dan Pranata Sosial Jurnal Elkolind : Jurnal Elektronika dan Otomasi Industri KESMAS UWIGAMA: Jurnal Kesehatan Masyarakat PINISI Discretion Review Didaktik : Jurnal Ilmiah PGSD STKIP Subang Indonesian Journal of Data and Science Academia Open GOVERNABILITAS (Jurnal Ilmu Pemerintahan Semesta) Mechonversio: Mechanical Engineering Journal JPBM - Journal of Policy and Bureaucracy Management Ruwa Jurai: Jurnal Kesehatan Lingkungan Jurnal Tika Ekosiana : Jurnal Ekonomi Syariah Malikussaleh Journal of Mechanical Science Technology International Journal of Economic, Business, Accounting, Agriculture Management and Sharia Administration (IJEBAS) Jurnal Ekonomi dan Manajemen Indonesia Golden Ratio of Social Science and Education Jurnal Riset Inossa : Media Hasil Riset Pemerintahan, Ekonomi dan Sumber Daya Alam Jurnal Neuroanestesi Indonesia JUSTICIA SAINS: Jurnal Ilmu Hukum Jurnal Ilmu Ekonomi Mulawarman (JIEM) Jurnal Basicedu Kybernology : Journal of Government Studies Golden Ratio of Data in Summary Multiverse: Open Multidisciplinary Journal AL-ISHLAH: Jurnal Pendidikan Islam Electronic Journal of Education, Social Economics and Technology Innovative: Journal Of Social Science Research JURNAL SANITASI LINGKUNGAN BEduManageRs Journal : Borneo Educational Management and Research Journal Jurnal Profesi Insinyur Indonesia Indonesian Journal on Data Science Teknomatika: Jurnal Informatika dan Komputer Journal of Language Education and Development (JLed) Journal of Contemporary Local Politics BEGIBUNG: Jurnal Penelitian Multidisiplin Jurnal Malikussaleh Mengabdi Sosial Simbiosis: Jurnal Integrasi Ilmu Sosial dan Politik Jurnal Vokasia MAXIMAL Jurnal Intelek Insan Cendikia Jurnal Solusi Masyarakat Dikara Spektra : Jurnal Ilmu-ilmu Sosial Malik Al-Shalih Abdi Karya: Jurnal Pengabdian Kepada Masyarakat ALMURTADO: Journal of Social Innovation and Community Service Journal of Mechanical Engineering and Fabrication SELAYAR: Jurnal Pengabdian Masyarakat As-Sulthan Journal of Education
Muhammad Habibi
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Journal : Indonesian Journal on Data Science

 1 
K-Nearest Neighbor and Naive Bayes Classifier Methods for Expedition Service Comparison Analysis of User Sentiments Nurul Hikmah; 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.v1i1.899

Abstract

Depending on the service chosen, expedition is one of the freight forwarding companies that operate in the domestic market. The availability of expedition services can make it easier for traders to transfer items to purchasers who conduct online transactions, as well as encourage shipping businesses to collaborate with online dealers. The JNE, JNT, and Pos Indonesia excursions were utilized in this study. The goal of this project is to develop an analytical model that will make it simpler for online merchants to find collaborators for effectively and securely transporting their goods. Based on user sentiment on the social networking site Twitter, this study uses sentiment analysis. With the keywords "JNT, JNE, and Pos Indonesia," this study compares the accuracy results using the Naive Bayes Classifier (NBC) and K-Nearest Neighbor (KNN) algorithms. According to this study, testing accuracy for the NBC method was 80% and training accuracy was 83%. While the accuracy of the KNN approach is 68%. According to public opinion, the JNE expedition is the best one for distributing products, scoring 68.58% in favor of it and 30.64% against it.
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.
K-Nearest Neighbor and Naive Bayes Classifier Methods for Expedition Service Comparison Analysis of User Sentiments Nurul Hikmah; 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.v1i1.899

Abstract

Depending on the service chosen, expedition is one of the freight forwarding companies that operate in the domestic market. The availability of expedition services can make it easier for traders to transfer items to purchasers who conduct online transactions, as well as encourage shipping businesses to collaborate with online dealers. The JNE, JNT, and Pos Indonesia excursions were utilized in this study. The goal of this project is to develop an analytical model that will make it simpler for online merchants to find collaborators for effectively and securely transporting their goods. Based on user sentiment on the social networking site Twitter, this study uses sentiment analysis. With the keywords "JNT, JNE, and Pos Indonesia," this study compares the accuracy results using the Naive Bayes Classifier (NBC) and K-Nearest Neighbor (KNN) algorithms. According to this study, testing accuracy for the NBC method was 80% and training accuracy was 83%. While the accuracy of the KNN approach is 68%. According to public opinion, the JNE expedition is the best one for distributing products, scoring 68.58% in favor of it and 30.64% against it.
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 | DOI: 10.30989/ijds.v2i1.1318

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.
Co-Authors . . A. Sinay, Stani Abd Rachim AF Abdul Rahman Abdul Syakur Abdul, Abdul Rachim Achmad Nurmandi Achmad Zaky Ade Ikhsan Kamil Adinda Thalia Salsabila Adityo Permana Wibowo admin admin, admin Aeni, Eni Nur Aflia Riski Agustin, , Debi Agustinar, Agustinar Agustinawati Agustinus Cahya Putra Ahmad Nayan ahmad yani Ahmad Yani Ainur Rachman, Ainur Aji Syarif Hidayatullah Akmal Akmal Al Badawi, M. Abu Amar Alchalil Alchalil Aldiansyah, Aldiansyah Alfath Khoir Nst Alfi Sa'diya, Nafisa Alfun Roehatul Jannah Ali Husni Alit Hindri Yani Ananda Putra, Alfin Annisa, Ajeng Nur Anton, La Apriyani Apriyani Apriyani, Apriyani Arbainah, Arbainah Ari Tri Fitrianto Arifin Abdullah Aris Wahyu Murdiyanto Armansyah Armansyah Armita, Desi Asrori Asrori Asyri, Derin Azzahra Shafa Salsabila Baehaqi BAGUS MUSTRIYANTO, ABIYOGA Budi Wardoyo Budi, Edi Sulistio Chalid, Ibrahim Chandra Chandra Christian, William Christover, Deandlles Cici Saputri DENY KURNIAWAN Destavino, Irvanus Devi Yanti Dewi Yulianti Bisri Dian Ratri Wulandari Dicky Reva Apriana Sanga Dwi Dimas Bayu Utomo Dwi Listyorini Dwijayanti, Irmma Eliyasni, Rifda Erdianto Effendi Eryani, Trisna Waty Riza Faisal Faisal Fajar Syahruddin Fani Aristianti fatimah Fatimah Faujiah, Ani Fauzi Akbar, Rizky Febriana, Nabilah Febriyana, Serly Ferri Safriwardy Fikal Setiawan Fresia Nandela, Emilia Hansen, Hansen Harni, Harni Hery Widijanto Hidayat, Heindrix Ikmal Maulana Ilhamda, Risky Ilzamudin Indrahadi, Deri Iqbal Hadi Subekti IRWANSYAH, MUHAMMAD SUTAN Jayawarsa, A.A. Ketut Jefri Andika johansyah johansyah Juanda Khalimatun Nuha Kharisma, Annisa Kristi, Ria Kusuma, Rizky Dwi Kusumaningtyas, Kartikadyota Lahitani, Alfirna Rizqi Lirim, Agustina Lynda Susana Widya Ayu Fatmawaty M. Abu Amar Al Badawi M. Ali Fikri M. Anas Nazarsyah Rahmatullah M. Hanif Ash Shiddiqi Mahadika, Alam Mahatir, Lunsa Avelia Masikah Shofi Kamila Masrullita, Masrullita Mastori, Mastori Miftahul Jannah Monika, Tiara Muammar Qaddafi Muhammad MUHAMMAD ALI Muhammad Ilham Muhammad Muhammad Muhammad Nuzan Rizki Muhammad Ruliansah Muhammad Sayuthi MUHAMMAD SUTAN IRWANSYAH Muhammad Wisnu Dwi Anggara Muhammad Zakki Mujianto, Eko Mujiburrahman Mujiburrahman Muliari Muliari Mursalin, M Mutiara Utami Muttaqin, Muchammad Zaenal Nabilah, Febriana Nova Rianti S Nugraha, Enung Nur Annisa Afifah Nur Azizah Firdausa Nur Kholan Karima Nurhayati Nurhayati Nurlaila, Rizka NURUL HIKMAH Oktaviana, Dela Permata, Jenny Meilila Azani Cahya Pitriani, Pipit Pramunings, Vita Pratama, Prawira Yudha Prawira, Komang Yehuda Puji Winar Cahyo Puji Winar Cahyo Putra, M. Iqbal Adhya Putra, Reza Rahman Yasin, Rahman Rahman, Prasetiya Budi Ramadani, Rusdiana Ramadhan, Muhammad Teddy Ekarizky Ramazalena, Rauzi Regina, Putri Ria Maya Sari Riadhus Sholihin Rianti. S, Nova Rifanny Br Girsang, Ribka Riki Amanda Rikza Sania Putri Rindu Oktavia Rizky Fauzi Akbar Rizky Meidiana, Dinda Rusdi Rusdi Rusman Rusman S.Natasya Carolina Safariah, Risna Saidi, Arbainah Satria Prayoga Sayuti, M. Sepriyadi Adhan S Septa Adri Fania Setiawan, Budhy Sirojuddin Abror Siti Rahmawati Sriliyus Agung Susilo Subekti, Dayat Sujono Riyadi Sukatin Sumiyarini, Retno Suriani, Ari Suryadi Suryadi Suswanta Syafril, Rizki Syahruddin, Fajar Syahyudi, Ilham Syamsiar, Syamsiar Syamsir Syamsir Syamsurralam, Alwi Tarigan, ⁠⁠Thomas Edyson Taruk, Medi Tasya Renanda Tatang Bisri Tegar Agustian Trisna Waty Riza Eryani Usman Usman Uyuni, Badrah Vebrianto, Rian Vita Pramaningsih Wahyu Ramadhan Wahyuningsiam, Astr Wardoyo, Budi Wasehudin Wibowo, Mohammad Fadhil Widia Astuti Widia Edorita Winar Cahyo, Puji Wulandari, Dian Ratri Yasir Amani Yearsi, Sri Evi New Yesi Anita Yulia, Ruka Yusuf Ahyar Sutaryono Zaini, Abdul Kudus Zulkifli, Said Zulmiardi, Zulmiardi