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All Journal Dinamik GEMA TEKNOLOGI Techno.Com: Jurnal Teknologi Informasi Jurnal Simetris Syntax Jurnal Informatika Elkom: Jurnal Elektronika dan Komputer Jurnal Ilmiah Mahasiswa FEB Prosiding SNATIF Jurnal Ketahanan Nasional Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Berkala Epidemiologi Seminar Nasional Informatika (SEMNASIF) CESS (Journal of Computer Engineering, System and Science) E-Dimas: Jurnal Pengabdian kepada Masyarakat JOIN (Jurnal Online Informatika) Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) SISFOTENIKA Journal of Information Technology and Computer Science (JOINTECS) JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Jurnal Ilmiah FIFO Jurnal Pilar Nusa Mandiri InComTech: Jurnal Telekomunikasi dan Komputer Prosiding Seminar Nasional Teknoka JRST (Jurnal Riset Sains dan Teknologi) JOURNAL OF APPLIED INFORMATICS AND COMPUTING SINTECH (Science and Information Technology) Journal JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Jurnal Sisfokom (Sistem Informasi dan Komputer) JURNAL EDUCATION AND DEVELOPMENT Jiko (Jurnal Informatika dan komputer) Explore IT : Jurnal Keilmuan dan Aplikasi Teknik Informatika Jurnal Telematika STRING (Satuan Tulisan Riset dan Inovasi Teknologi) CCIT (Creative Communication and Innovative Technology) Journal Journal of Information System, Applied, Management, Accounting and Research Jurnal Ilmiah Ilmu Komputer Fakultas Ilmu Komputer Universitas Al Asyariah Mandar Journal of Electronics, Electromedical Engineering, and Medical Informatics Jurnal Ilmu Komputer dan Bisnis Syntax Idea Techno Xplore : Jurnal Ilmu Komputer dan Teknologi Informasi Jurnal Sistem informasi dan informatika (SIMIKA) Jurnal Mnemonic Jurnal Asiimetrik: Jurnal Ilmiah Rekayasa Dan Inovasi Journal of Computer Science and Engineering (JCSE) SKANIKA: Sistem Komputer dan Teknik Informatika Media Gizi Kesmas Jurnal Teknik Informatika (JUTIF) Jurnal Pewarta Indonesia JURNAL KOMUNIKASI DAN BISNIS Ascarya: Journal of Islamic Science, Culture and Social Studies Jurnal PkM (Pengabdian kepada Masyarakat) Humantech : Jurnal Ilmiah Multidisiplin Indonesia Media Publikasi Promosi Kesehatan Indonesia (MPPKI) Bit (Fakultas Teknologi Informasi Universitas Budi Luhur) Journal Of Human And Education (JAHE) Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) Jurnal Algoritma Jurnal Ticom: Technology of Information and Communication Berita Kedokteran Masyarakat Journal of Systems Engineering and Information Technology J-Icon : Jurnal Komputer dan Informatika Jurnal Teknik Indonesia Research Horizon Jurnal Relawan dan Pengabdian Masyarakat REDI Jurnal Pengabdian Masyarakat Nasional Health Dynamics Jurnal Ticom: Technology of Information and Communication The Indonesian Journal of Computer Science Seminar Nasional Riset dan Teknologi (SEMNAS RISTEK) Prosiding SeNTIK STI&K Journal of Medical and Health Science Jurnal Ilmu Kesehatan Immanuel Jurnal Analogi Hukum
Arief Wibowo
Universitas Budi Luhur
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Journal : SINTECH (Science and Information Technology) Journal

 1 
IMPLEMENTASI ALGORITMA NAIVE BAYES UNTUK MEMPREDIKSI FREKUENSI TUNAI PADA MESIN ATM DI MASA TRANSISI PEMBATASAN SOSIAL BERSKALA BESAR (PSBB) PANDEMI COVID-19 Saptari Wijaya Mulia; Sujiharno Sujiharno; Arief Wibowo
SINTECH (Science and Information Technology) Journal Vol. 4 No. 1 (2021): SINTECH Journal Edition April 2021
Publisher : LPPM STMIK STIKOM Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31598/sintechjournal.v4i1.622

Abstract

Determining the need of money for ATM is usually different, that is one of the problems in managing money allocation of ATM. Some seasonal factors such as holidays and the implementation of transition large-scale social restrictions related to the covid-19 pandemic that can affect fluctuations in cash transactions. In this paper aims to determine the frequency of cash withdrawals at ATM since the enactment of transition large-scale social restrictions in Jakarta using the naive bayes algorithm so it can be identified which ATM require more allocation money or not. Providing the right money allocation can improve the quality of service to customers and minimize unused money in ATM. Results of analysis using a Naive Bayes algorithm to predict cash withdrawals frequencies at ATM that show a prediction accuracy up to 81%
Komparasi Naïve Bayes dan SVM Analisis Sentimen RUU Kesehatan di Twitter Widyanto, Tetrian; Ristiana, Ina; Wibowo, Arief
SINTECH (Science and Information Technology) Journal Vol. 6 No. 3 (2023): SINTECH Journal Edition December 2023
Publisher : Prahasta Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31598/sintechjournal.v6i3.1433

Abstract

This research focuses on sentiment analysis regarding the plan to ratify the Health Bill which has become a hot topic of conversation on social media, especially Twitter. This research aims to classify tweets that reflect various opinions regarding the Health Bill, including support, rejection and neutrality. In this research, the author uses two types of classification algorithms, namely the Multinomial Naïve Bayes Algorithm and the Support Vector Machine (SVM) Algorithm. Previously, tweets were labelled using the Lexicon InSet dictionary. The research was conducted in the Python programming language and using Google Collaboratory. Data validation was carried out using the K-fold cross-validation method. The research results indicate that both algorithms predominantly produce positive sentiments over negative ones. However, SVM with a linear kernel achieves a higher accuracy rate of 0.87, compared to Multinomial Naïve Bayes, which has an accuracy of 0.82. SVM also records a precision of 0.87, recall of 0.97, and an F1-score of 0.91, while Multinomial Naïve Bayes shows a precision of 0.81, recall of 0.98, and an F1-score of 0.89. Overall, this research confirms that SVM excels in text sentiment classification, while Multinomial Naïve Bayes also provides good results in recognising positive and negative sentiment. These results have important implications for understanding public sentiment regarding the Health Bill on the Twitter platform.
Perbandingan Kinerja Algoritma K-Means dan K-Medoids Dalam Klasterisasi Jumlah Tindak Pidana Kejahatan Berbasis Wilayah Kepolisian Daerah Nurcahya, Gelar; Wibowo, Arief; Kristanto, Dwi
SINTECH (Science and Information Technology) Journal Vol. 6 No. 3 (2023): SINTECH Journal Edition December 2023
Publisher : Prahasta Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31598/sintechjournal.v6i3.1457

Abstract

Criminal acts are often a problem that occurs in Indonesia. Where currently the number of reports handled by the police regarding criminal acts is always there every day. Indonesia's population is increasing and the background of perpetrators who are unemployed is often one of the reasons why the police find it difficult to resolve criminal acts that occur due to limited human resources. To overcome this problem, information is needed that provides areas in Indonesia where criminal acts frequently occur so that the police can make decisions to allocate human resources to protect those jurisdictions from criminal acts that occur. Using data on criminal offenses and the employment of criminal offenders, namely not working from 2021, data was taken from the National Police Criminal Investigation Unit's Pusiknas Annual Journal. The data will be clustered using data mining techniques using the K-Means and K-Medoids algorithms. These 2 algorithms produced 2 clusters with the smallest Davies Bouldin index value found in the K-Means algorithm with a value of 0.272. With the research results which produced 2 clusters, it can be concluded that there are categories of high crime and low crime.
Perbandingan Metode K-Medoids dan Metode K-Means Dalam Analisis Segmentasi Pelanggan Mall Rohman, Nur; Wibowo, Arief
SINTECH (Science and Information Technology) Journal Vol. 7 No. 1 (2024): SINTECH Journal Edition April 2024
Publisher : Prahasta Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31598/sintechjournal.v7i1.1507

Abstract

Memahami pelanggan sangat penting untuk mengelola operasi perusahaan. Dengan mengetahui dan memahami setiap pelanggan, dapat meningkatkan komunikasi layanan produk dengan menyesuaikan kebutuhan dan layanan kepada setiap pelanggan. Namun, analisis pelanggan sangat luas sehingga sulit untuk memahami kebutuhan masing-masing pelanggan. Hal ini dapat mencakup berbagai karakteristik dan perilaku pelanggan. Oleh karena itu, diperlukan segmentasi pelanggan untuk mengelompokkan pelanggan berdasarkan perilaku dan karakteristiknya. Tujuan dari penelitian ini adalah membandingkan metode clustering untuk mendapatkan metode yang lebih baik dan optimal dalam mengelompokkan cluster untuk segmentasi pelanggan. Dari permasalahan tersebut, peneliti menerapkan metode CRISP-DM dengan focus pada analisis cluster atau pengelompokkan dengan membandingkan algoritma K-Means dan K-Medoids terhadap Analisa segmentasi pelanggan pada mall. Pada penerapan perbandingan metode K-Means dan K-Medoids, digunakan metode elbow untuk menentukan jumlah cluster yang optimal. Hasil dari metode elbow menunjukkan bahwa penggunaan lima cluster untuk metode K-Means dan empat cluster untuk metode K-Medoids merupakan pilihan yang tepat dalam kasus ini. Langkah selanjutnya adalah mencari nilai Silhouette Coefficient setiap metode yang digunakan dalam perbandingan untuk menentukan metode clustering yang lebih optimal.  Hasil nilai yang diperoleh dari metode Silhouette Coefficient masing-masing metode adalah k-means adalah 0,553 dan k-medoid adalah 0,485, sehingga algoritma pengelompokan segmentasi pelanggan terbaik pada penelitian ini adalah algoritma K-means karena memiliki nilai koefisien siluet maksimum.
Co-Authors - Arientawati - Sumardianto Achadi, Abdul Haris Adita, Ita Afifah Khaerani Afifatussalamah, Rizka Ahmad Sururi Ahmad Sururi Akbar, Ahmad Aldizar Al Fatach, M Khabib Anggraini, Julaiha Probo Anita Diana Anugrah Sandy Yudhasti Apriati Suryani Ardhianto, Angga Ardianah, Eva Ari Wibowo Arief Umarjati Asep Permana Atik Ariesta Bayu Sadewo Bayu Satria Pratama Binarto, Antonius Jonet Bintang, Bagus Boerhan Hidayat, Boerhan Danar Wido Seno Danniswara, Ahmad Darki, Ni Wayan Yustika Agustin Deni Mahdiana Diah Indriani Didik Hariyadi Raharjo Didin Muhidin Dwi Kristanto Dwi Yulianti Dyah Retno Utari Dyah Retno Utari, Dyah Retno Ebine, Masato Endah Sarah Wanty Fajar Siddik Chaniago Farah Chikita Venna Farid Setiawan Farid Setiawan, Farid Febrilliani, Jihan Sastri Fenny Irawati Fernando, Donny Firman Noor Hasan Firmanty Mustofa, Vina Fitri Nur Masruriyah, Anis Fitri Rachmilah Fadmi Fitriadi, Rifqi Fitriani, Netty Fransiska Vina Sari Frenda Farahdinna Fried Sinlae Ghapur, Abdul Gurdani Yogisutanti Hadidtyo Wisnu Wardani Hananto, Agustia Handoko, Andy Rio Hanindita, Meta Herdiana Hari Basuki Notobroto Haris Achadi, Abdul HARIYANTO HARIYANTO Harun Nasrullah Hayatul Khairul Rahmat Henry Henry Herriyawan, Herriyawan Hidayat, Manarul Hidayat, Sarifudlin Huda, Ratu Najmil I MADE MINGGU WIDYANTARA, I MADE MINGGU Indah Rizky Mahartika Inge Virdyna Irfan Hadi Irfan Nurdiansyah Istiqoomatun Nisaa Iwan Irawan Jasmine, Meuthia Joko Sutrisno Jovansgha Avegad Jumaryadi, Yuwan Kanasfi, Kanasfi Karma, Ni Made Sukaryati Karyaningsih, Dentik KRESNO YULIANTO Kresno Yulianto KUNTORO Kuntoro Kuntoro Kurnia Setiawan Kutanto, Haronas Larasati, Pamela Linda Lingga Desyanita Luthfi Akbar Ramadhan Mahmudah Mahmudah Mailana, Agus Maria Adiningsih Marlina, Hesti Martens, Brigitta Griselda Maskur A, Moch Riyadi Megananda Hervita Permata Sari Megawati, Rina Miftahul Arifin Miftahul Arifin Mochammad Rizky Royani Moh Makruf Monica, Silvi Muhamad Fadel Muhammad Bagus Bintang Timur, Muhammad Bagus Bintang Muhammad Febrian Rachmadhan Amri Muhammad Risky Mulyati Mulyati Nazihah, Fasya Nendi, Nendi Ningrum, Yogi Ajeng Nugroho, Angelika Pratiwi Widya Nur Aisiyah Widjaja, Nur Aisiyah Nur Rohman Nurcahya, Gelar Nurfadhiilah, Annisa Nurfidaus, Yasmine Nursyi, Muhamad Pattipeilohy, William Frado Pattipeilohy, William Frado Pebriaini, Prisma Andita Poppy Ruliana Pradiptha, Anindya Putri Probo Anggraini, Julaiha Purwadi Purwadi Putra, Andi Agung Putra, Rinaldi Febryatna Duriat Rachmah Indawati Rahman, Fathin Aulia Rahman, Reza Rahmawati, Nur Anisah Rakhman, Abdulah Rakhmat Rakhmat Rakhmat Rakhmat RAMAYU, I Made Satrya Rangkuti, Muhammad Yusuf Rizqon Ratna Ayu Sekarwati Ratna Ayu Sekarwati Relawanto, Bowo Ria Puspitasari Rika Nurhayati Riki Ramdani Saputra Rina Megawati Ririh Yudhastuti Risaychi, Diva Ajeng Brillian Ristiana, Ina Riza, Yeni Rizkiyanto, Muhamad Ardiansyah Roedi Irawan Rojakul, Rojakul Rosita Dewi, Erni Ruliana, Poppy Rusdah Ryo Tanaka Sabirin, Sahril Sadewo, Bayu Santoso, Febrina Mustika Saptari Wijaya Mulia Sari Anggar Kusuma Melati Sari, Fransiska Vina Sasongko, Raden Satiri Satiri, Satiri Selly Rahmawati Selly Rahmawati Septian Firman S Sodiq Septiani, Riska Setya Haksama Setyowati, Erlin Shofinurdin Shofinurdin Siddik Chaniago, Fajar Sigit Ari Saputro Sigit Budi Nugroho Siregar, Sutan Syahdinullah SITI NURUL HIDAYATI Sitti Aliyah Azzahra Soenarnatalina Melaniani Sudewo, Andika Hasbigumdi Sugiyarta, Ahmad Sujiharno Sujiharno Sumarna, Presma Dana Scendi Suntoro, Dimas Fahmi Tiaharyadini, Rizka Triantoro, Ery TRISNAWATI, WULAN Tulus Yuniasih Umam, Mohamad Hafidhul Vasthu Imaniar Ivanoti Wahyu Cesar Wahyu Desena Wahyudi, Widi Wahyuni, Chatarina Unggul Wangsajaya, Yosia Heartha Dhalasta Wasis Budiarto Wibiyanto, Alif Dewan Daru Widiyaningrum, Diyah Kiki Widyanto, Tetrian Windhu Purnomo Yahya Darmawan Yudanto, Satyo Zakaria Anshori Zaqi Kurniawan