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All Journal Teknologika Jurnal Informatika Teknologi dan Sains (Jinteks) RISTEC : Research in Information Systems and Technology Sistematis
Muhammad Rafi Muttaqin
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Industrial & Manufacturing Engineering Mechanical Engineering Other

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PENENTUAN MEDIA PROMOSI STT WASTUKANCANA PURWAKARTA MENGGUNAKAN METODE WEIGHTED AGGREGATED SUM PRODUCT ASSESMENT (WASPAS) Intan Nopita; Muhammad Rafi Muttaqin; Nurfitriansyah
Jurnal Informatika Teknologi dan Sains Vol 4 No 3 (2022): EDISI 13
Publisher : Program Studi Informatika Universitas Teknologi Sumbawa

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (451.842 KB) | DOI: 10.51401/jinteks.v4i3.1877

Abstract

Promotion is an element used to informand persuade the market about a new product or service to the companythrough an advertisement. However, the large selection of promotional media at this time makes us required to continue to make decisions properly, precisely and also quickly, and accountable. This study aims to create a decision support system in choosing promotional media at the Wastukancana Purwakarta Cpllege of Teknology (STT) with the Weighted Aggregated Sum Product Assesment method. Where the alternatives and criteria have been determind by the Wastukancana STT Promotion Team. There are (five) criteria that have been determined, namely financing,completeness of information, promotional content, reac,and flexibility of acces. As well as for alternatives, namely brochures,banner, socialization,social media and websites. The software didevelopment method used is waterfaal. This application is buit using PHP Codeigniter and MYSQL, then the testing method uses Black Box Testing. The calculation result put social media as the most recommended alternative promotional media.
APPLICATION OF THE NAÏVE BAYES ALGORITHM FOR PREDICTION OF LUNG DISEASES USING RAPIDMINER Muhyidin, Yusuf; Muhammad Rafi Muttaqin; Imam Ma'ruf Nugroho; Moch. Hafid
Jurnal Teknologika Vol 14 No 1 (2024): Jurnal Teknologika
Publisher : Sekolah Tinggi Teknologi Wastukancana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51132/teknologika.v14i1.378

Abstract

The lungs are the main organ in the human respiratory system which is located in the chest cavity and consists of a pair. The lungs are a vital organ that greatly influences the body's health, because it has the function of maintaining the body's acid-base balance, removing carbon dioxide that the body does not need and water vapor. Smoking is the main cause of lung disease, in 2020 based on the World Health Organization (WHO) report, it is estimated that 10 million people suffer from lung disease worldwide. This research uses the Naïve Bayes classification algorithm to obtain a prediction model that can predict lung disease patient data. This research aims to obtain accuracy values ​​using the Naïve Bayes algorithm. The data used in this research was obtained from Kaggle which contains 469 data with 14 attributes in it. RapidMiner is used as a tool to test the patient dataset used to produce a prediction with an accuracy rate of 99.9% risk false (no risk of having lung disease).
Sentiment Analysis of the LinkedIn Application Using the Lexicon Based Meth¬od Based on Google Play Store Reviews Meriska Defriani; Muhammad Rafi Muttaqin; Qonita Rizkiya Karima
RISTEC : Research in Information Systems and Technology Vol. 5 No. 1 (2024): JURNAL RISTEC : Research in Information Systems and Technology
Publisher : RISTEC : Research in Information Systems and Technology

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Abstract

LinkedIn is the largest professional network in the internet world. Useful for finding the right job, strengthening professional relationships, learning skills as needed for a successful career and having the opportunity to expand relationships. In this study, we will analyze the sentiment of android users towards one of the applications in the google play store, namely LinkedIn, through reviews in the google play store comments column. The research method is lexicon based using the vader sentiment library to determine the value of whether the sentence is in the positive, negative or neutral class. Based on the results of the percentage accuracy obtained that android users of the LinkedIn application on the google play store using the lexicon based method by utilizing the vader sentiment library, namely 56.56% for the positive sentiment class, 30.62% for the neutral sentiment class and 12.82% for the negative sentiment class. This shows that positive sentiment is greater than negative and neutral sentiment. Keywords : LinkedIn; google play store; lexicon based; library vader sentiment
Pemanfaatan Algoritma Convolutional Neural Network Dengan Untuk Mendeteksi Penyakit Pada Tumbuhan Jagung Alfin Kabir; Muhammad Rafi Muttaqin; Dede Irmayanti
Sistematis Vol. 1 No. 1 (2024): Oktober 2024
Publisher : CV.RIZANIA MEDIA PRATAMA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69533/4yvkhd74

Abstract

Jagung adalah salah satu jenis tanaman serealia yang memiliki peran yang sangat penting dalam penyediaan bahan pangan karena kandungannya yang kaya akan karbohidrat. Namun, jumlah produksi jagung pada tahun 2023 mengalami penurunan dibandingkan pada tahun 2022. Salah satu faktor yang menyebabkan menurunnya produksi jagung adalah serangan hama dan penyakit pada tanaman jagung. Dengan perkembangan teknologi pengolahan citra digital dan pembelajaran mesin, diagnosis penyakit tanaman dapat dilakukan dengan lebih cepat dan akurat. Penelitian ini menggunakan dataset citra daun jagung yang mengandung berbagai jenis penyakit. Citra-citra tersebut diproses dan diolah oleh sebuah model dengan algoritma convolutional neural network (CNN) yang telah melalui proses fine-tuning hyperparameter. Penelitian ini membuktikan bahwa penggunaan metode convolutional neural network dengan fine-tuning hyperparameter memberikan hasil yang memuaskan dalam mengklasifikasikan jenis penyakit pada tanaman jagung. Hasil penelitian menunjukkan bahwa model ini mampu mencapai nilai akurasi sebesar 0.92 dan nilai loss sebesar 0.363. Temuan ini menunjukkan potensi besar penggunaan CNN dalam mendeteksi penyakit pada tanaman jagung, yang dapat membantu petani dalam meningkatkan produksi dan kualitas hasil panen mereka.
Co-Authors Alfin Kabir Dede Irmayanti Imam Ma'ruf Nugroho Intan Nopita Meriska Defriani Moch. Hafid Muhyidin, Yusuf Nurfitriansyah Qonita Rizkiya Karima