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All Journal Media Mahardhika Aptisi Transactions on Management Jurnal Gentala Pendidikan Dasar Jurnal Teknik dan Informatika JURNAL ILMIAH EDUKATIF Keraton: Journal of History Education and Culture Bioscientist : Jurnal Ilmiah Biologi Jurnal Ekonomi Bisnis, Manajemen dan Akuntansi (JEBMA) Instal : Jurnal Komputer INTERNATIONAL JOURNAL OF CULTURAL AND SOCIAL SCIENCE Bulletin of Information Technology (BIT) Jurnal Nasional Teknologi Komputer Jurnal Budgeting Jurnal Abdimas Hawari Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Jurnal Masyarakat Indonesia Al-Hisbah Jurnal Ekonomi Syariah Ramatekno Jurnal Pembelajaran Bahasa dan Sastra Tabayyun : Journal of Islamic Studies Journal of Environmental and Development Studies Bulletin of Engineering Science, Technology and Industry Jurnal Sains dan Teknologi Prosiding Seminar Nasional Keguruan dan Pendidikan (SNKP)
Khairul
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Journal : Bulletin of Information Technology (BIT)

 1 
Implementasi Sistem Pendukung Keputusan dalam menentukan Kecamatan Terbaik Menggunakan Algoritma Entropy dan Additive Ratio Assessment (ARAS) Ernawati, Andi; Ofta Sari, Ayu; Sofyan, Siti Nurhaliza; Aulia, Ananda; Sitorus, Zulham; Khairul
Bulletin of Information Technology (BIT) Vol 4 No 4: Desember 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v4i4.1066

Abstract

In the context of regional development and decision making related to determining the best village, the use of a Decision Support System (DSS) with the application of the Entropy and Additive Ratio Assessment (ARAS) algorithms is a very important approach. The main objective of this research is to propose and implement a method that utilizes the Entropy algorithm to evaluate criteria weights and ARAS to rank villages based on predetermined criteria. This approach begins the process by identifying relevant criteria to determine the best village in an area. Next, the Entropy algorithm is used to measure the level of importance or relative weight of each predetermined criterion. This step helps in assessing how informative each criterion is in the decision-making process regarding determining the best Village. After determining the criteria weights using Entropy, the approach continues with the application of the ARAS method. ARAS is used to rank villages based on normalized values ​​from previously determined criteria. The data normalization process is carried out to ensure the validity of comparisons between villages. The final result of this approach is a ranking of villages indicating the best villages based on the criteria considered. This method was tested in a case study using a dataset involving a number of relevant criteria for assessing village development potential. Experimental results show that the use of the Entropy and ARAS algorithms in the Decision Support System provides an effective and informative framework for decision makers in determining the best Village. In conclusion, this approach provides a solid foundation to support a more effective and precise decision-making process in regional development based on clearly defined criteria.
Penerapan Metode Certainty Factor Pada Sistem Pakar Diagnosa Penyakit Gigi Dan Mulut Zalukhu, Anzas Ibezato; Irwan Syahputra; Suhardiansyah; Sitorus, Zulham; Khairul
Bulletin of Information Technology (BIT) Vol 4 No 4: Desember 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v4i4.1102

Abstract

Gigi dan mulut merupakan organ vital yang memainkan peran penting dalam menjaga kesehatan manusia. Kelainan pada gigi dan mulut dapat menjadi pemicu penyakit lain dalam tubuh. Pentingnya menjaga kesehatan gigi dan mulut ditekankan, terutama mengingat fungsinya yang esensial dalam berbicara, menjaga bentuk wajah, dan mengunyah makanan. Sayangnya, dengan perkembangan zaman, pola makan yang tidak sehat, seperti konsumsi makanan siap saji tinggi gula, garam, dan lemak, dapat menyebabkan masalah kesehatan gigi dan mulut. Penyakit gigi dan mulut sering disebabkan oleh mikroorganisme, dan pengetahuan terbatas tentang gejala-gejala penyakit ini dapat menjadi hambatan untuk diagnosis dini. Sebagai solusi, penelitian ini mengusulkan penerapan metode certainty factor dalam sistem pakar untuk mendiagnosis penyakit gigi dan mulut. Metode ini memungkinkan evaluasi tingkat keyakinan pakar terhadap data yang dianalisis, memberikan solusi atau rekomendasi dalam situasi kompleks. Penelitian ini mengacu pada pandangan pakar dokter gigi dan mulut, yang dianggap memiliki pengetahuan dan pengalaman yang mencukupi. Sistem pakar yang diusulkan bertujuan untuk meniru proses penalaran seorang pakar dalam memecahkan masalah spesifik dalam bidang gigi dan mulut. Dengan memanfaatkan certainty factor, sistem ini dapat menyediakan solusi yang lebih dapat diandalkan dan memberikan kontribusi pada upaya pencegahan serta penanganan dini penyakit gigi dan mulut.
Penerapan Data Mining Untuk Klasifikasi Penduduk Miskin Di Kabupaten Labuhanbatu Menggunakan Random Forest Dan K-Nearest Neighbors Ernawati, Andi; Khairul; Sitorus, Zulham; Iqbal, Muhammad; Nasution, Darmeli
Bulletin of Information Technology (BIT) Vol 6 No 2: Juni 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v6i1.1783

Abstract

This study aims to apply and compare the performance of two data mining algorithms—Random Forest (RF) and K-Nearest Neighbors (KNN)—in classifying poverty status among residents of Labuhanbatu Regency. The dataset includes information on occupation, income, housing, and education from 21,137 individuals. After undergoing preprocessing, model training, hyperparameter optimization, and evaluation, both models were assessed using five key metrics: accuracy, precision, recall, F1-score, and AUC. The results show that Random Forest performed slightly better than KNN, achieving an accuracy of 0.6023, precision of 0.4827, recall of 0.4177, F1-score of 0.4479, and an AUC of 0.5681. In comparison, KNN obtained an accuracy of 0.5990, precision of 0.4771, recall of 0.4006, F1-score of 0.4355, and an AUC of 0.5622. Based on these findings, it can be concluded that Random Forest is more effective for poverty classification on this dataset, although the performance difference is relatively small.
Co-Authors Abdul Pandi Ade Surya Bakti Pane Adim, Saiful Alex Siregar Alviona Marsya Andi Ernawati Andysah Putera Utama Siahaan Annisa Ari Anshari Yuliansyah Siregar Astri Mutia Rahma Aulia, Ananda Ayu Husniyyah Baehaqi Boy Rizki Akbar Chelfina Utami Darmeli Nasution Devani Dewi Triana Diva, Krisna Eka Putra Erbin Sitorus Eswin Syahputra Farta wijaya, Rian Fatimah Fery Anugerah Fitriani Fitriany, Lestari Gusmi Hamzah, Iswadi Harmiati Bungsu Bangun Harry Wiryatama Hasmi Syahputra Harahap, Hasmi Syahputra Heryati Ilmiah, Putri Khairil Irfan Nainggolan Irvan Malay Irwan Syahputra Iwan Purnama Jantianus - Laila Maghfirah Leni Marlina M. Azwan M. Rasyid Mentari, Risca Sri Mochamad Heru Riza Chakim Muhammad Fahriza Muhammad Iqbal Muhammad Irsyad Muhammad Rachmat Mulyati Mulyati Mulyati Naibaho, Dameria Ndona, Yakobus Nurmatias, Faizal Ofta Sari, Ayu Permata Sari, Indah Putra Nauli Harahap, Sutan Rian Farta Wijaya RINI INDAHWATI Rio Septian Hardinata Rosmaida Tambun Rumiyatun Rusdi Machrizal Samsuddin Sari, Novi Fitriandika Sholihin siti fadilah Sitorus, Zulham Situmorang, Zenfitri Sofyan, Siti Nurhaliza Sri Yunita Suhardiansyah Suseno, Doni Syafi’I Syahputra, Dicky Ananda Syifa Tegar Ramadhan Triyadi, M Dico Tuahman Turnip Tuti Andriani Wahyungsih, Hesti Widodo Widodo Wijaya, Rian Farta Wiryatama, Harry Wulan, Nawang Yusinta Tia Rusdiana zalina, indah Zalukhu, Anzas Ibezato Zebua, Selamat Zulfahmi Syahputra