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All Journal PITIN JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) INFOKUM Jurnal Ilmu Komputer dan Sistem Informasi (JIKOMSI) KLIK: Kajian Ilmiah Informatika dan Komputer
Mahdianta Pandia
STMIK Kristen Neumann Indonesia, Medan
Computer Science & IT Decision Sciences, Operations Research & Management

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Naïve Bayes Classifier and Decision Tree Algorithms for Classifying Payment Data Berti Sari Br Sembiring; Mahdianta Pandia; Harianta Sembiring; Desi Margaretta
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 1 (2023): Agustus 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i1.963

Abstract

In this study the authors will analyze the comparison of the naïve Bayes classifier and decision tree methods in the classification of transaction data types of payments that are often made by customers where the method will analyze which model has the best percentage. The author uses the Kaggle deals payment data set. The data mining methods used to classify data are naïve Bayes classifier, decision tree, and rule based. For this study the Naïve Bayes Classifier method will be used. The results of the research on the accuracy of data classification using a decision tree have an accuracy value of 95.60%. where the predicted data yes with yes answers totaled 232 and answer no 17 with a class precision value of 93.17%. While the predictions for no with yes answers totaled 5 and for answers no totaled 246 with a class precision value of 93.17%. Based on the results of research using the naïve Bayes classifier and decision tree, it is possible to classify data on types of deals payment based on age ranges with different accuracies. From the percentage results, the decision tree method has the highest or best percentage with a value of 95.60%, while the Naïve Bayes classifier has a value of 92.20%.
IOT PEMANFAATAN INTERNET OF THINGS PADA SMARTHOME DENGAN MODEL SIMULASI PROTOTYPE: IOT Kurnia, Eka; Pandia, Mahdianta; Sembiring, Berti Sari Br; Margaretta, Desi
Jurnal Ilmu Komputer dan Sistem Informasi (JIKOMSI) Vol. 7 No. 1 (2024): Jurnal Ilmu Komputer dan Sistem Informasi
Publisher : Utility Project Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55338/jikomsi.v7i1.2728

Abstract

Internet of Things has been a recent focus in technology discussion. This tecnology enables every devices we own to connect to the internet, allowing remote control throught smartphones or even voice commands. The development of iot extends beyond large industries like power grids and factories, reaching into everyday household electronics. For instance, light can now be connected to the internet. The final project involves designing and creating an internet controlled relay system with comamands sent throught a telegram bot. This project aims to implement iot tecnology in a simple and affordable manner. The control system utilizes the nodeMCU microcontroller with a two -channel relay output to remote turn light on and off via telegram.
Kajian Literatur Multimedia Retrieval : Machine Learning Untuk Pengenalan Wajah Pandia, Mahdianta
Jurnal Ilmu Komputer dan Sistem Informasi (JIKOMSI) Vol. 7 No. 1 (2024): Jurnal Ilmu Komputer dan Sistem Informasi
Publisher : Utility Project Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55338/jikomsi.v7i1.2758

Abstract

Multimedia retrieval adalah pencarian dan temu kembali mengenai informasi yang terkandung pada konten multimedia. Konten multimedia terdiri dari gambar, teks, video, suara atau kombinasi dari keempatnya. Konten multimedia khususnya foto diupload lebih dari lebih dari 1,2 triliun foto dan video digital diambil setiap tahunnya. Delapan puluh lima (85) % dari konten multimedia tersebut diambil menggunakan smartphone dan langsung di upload ke media sosial. Penumpukan konten multimedia akan terus bertambah setiap tahunya, sehingga memutuhkan waktu untuk menelusurinya kembali di media penyimpanan yang digunakan. Multimedia retrieval dapat mengklasifikasikan koten tersebut berdasarkan pemilik wajah dari konten tersebut. Pengenalan wajah dapat dilakukan dengan baik menggunakan artificial intelligence. Perkembangan artificial intelligence juga terus berkembang hingga munculnya teknologi machine learning. Saat ini banyak dilakukan penelitian mengenai multimedia retrieval menggunakan machine learning yang didukung dengan algoritma AI yang lain, seperti deep learning. Pada kajian literatur ini akan melakukan kajian tentang multimedia retrieval, machine learning dan algoritma yang digunakan dalam penegnalan wajah sehingga diproleh kesimpulan Tingkat keberhasilan metode multimedia retrieval dan machine learning untuk mengenali wajah.
Co-Authors Berti Sari Br Sembiring Berti Sari Br Sembiring Berti Sari Br Sembiring Desi Margaretta Devanta Abraham Tarigan Eka Kurnia, Eka Evi Florentina Br Ginting Fransisca Br Sebayang Friendly Friendly Harianta Sembiring Harianta Sembiring Krismonika Br Sitepu Lidia Megawati Br Kembaren Margaretta, Desi Natalina Br Sitepu Pengarapenta Bangun Poltak Sihombing Purwanto Simamora Roberto Kaban Sembiring, Berti Sari Br Zakaria Sembiring