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All Journal Jurnal Sistem Komputer & Kecerdasan Buatan Engineering, Mathematics and Computer Science Journal (EMACS) Sisfo: Jurnal Ilmiah Sistem Informasi
Budi Juarto
Bina Nusantara University
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Library & Information Science Mathematics Mechanical Engineering

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Safe-Deposit Box Using Fingerprint and Blynk Yulianto Yulianto; Budi Juarto; Ika Dyah Agustia Rachmawati; Risma Yulistiani
Engineering, MAthematics and Computer Science (EMACS) Journal Vol. 4 No. 1 (2022): EMACS
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v4i1.8080

Abstract

The criminal act of robbery really makes people nervous, especially in urban areas. There are many ways that can be done to avoid robbery at home and office, such as increasing the security system in the house to protect valuables. Safe-deposit boxes are items that are used to store valuables. Safe-deposit box is used to prevent against theft who want to take valuable things. To increase security, technology has begun to develop for security in various ways, such as fingerprints, passwords, and buzzers. This research will focus on a safe security system using a fingerprint that is connected to the internet with the Blynk application so that the user will get a safe notification when the servo condition is open or closed. The fingerprint sensor is an access to open doors, the Arduino Uno microcontroller is a storage for command logic on the system, the stepper motor acts as an activator for opening and closing servo and the Esp8266 module as a Wi-Fi module that connects equipment components using the internet network with the Blynk application which is used as distance control and notification of incoming access to homes with the concept of Internet of Things (IoT).
Klasifikasi HIV AIDS dengan Aplikasi Rapid Miner Aden Wahyu P.; Rizky A. Susanto; Alfian R Putra; Felix Indra Kurniadi; Budi Juarto
Jurnal SISKOM-KB (Sistem Komputer dan Kecerdasan Buatan) Vol. 6 No. 1 (2022): Volume VI - Nomor 1 - September 2022
Publisher : Teknik Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47970/siskom-kb.v6i1.320

Abstract

Abstract— HIV adalah virus yang menyerang sistem kekebalan tubuh yang selanjutnya meningkatkan kemampuan tubuh untuk melawan infeksi dan penyakit. Sejarah AIDS Virus HIV dikatakan berasal dari Kinshasa, Republik Demokratik Kongo. Pada saat itu, para ahli percaya bahwa HIV berasal dari spesies simpanse yang ditularkan ke manusia. Pada simpanse, virus tersebut diberi nama Simian Immunodeficiency Virus atau SIV. Sebelum kemudian menyebabkan penularan HIV pada manusia, penularan virus simpanse ini mungkin berasal dari perburuan simpanse untuk diambil dagingnya, kemudian para pemburu tersebut terkena darah hewan yang terinfeksi. Studi oleh Pusat Pencegahan dan Pengendalian Penyakit (CDC) menunjukkan bahwa HIV mungkin telah ditularkan dari simpanse ke manusia sejak akhir 1800-an. Kinshasa adalah kota terbesar di Kongo, kota dengan pertumbuhan tercepat dengan jaringan transportasi yang menjangkau seluruh negeri. Sebuah laporan menyebutkan sejarah di balik penularan HIV AIDS dari Kongo ke seluruh dunia. Maraknya perdagangan seks, pertumbuhan penduduk, dan jarum suntik yang tidak steril di klinik-klinik diduga menjadi penyebab penyebaran virus HIV yang cukup pesat saat itu. Sejarah juga mencatat AIDS kemudian merajalela di Amerika, Eropa, lalu ke seluruh dunia. Untuk memeriksa data yang ada kami menggunakan aplikasi Data Miner untuk memudahkan kami dalam memeriksa data tersebut. masih banyak pasien yang terpapar virus HIV yang artinya masih banyak masyarakat yang tidak sadar akan bahaya virus yang jika tidak segera ditangani virus ini akan memasuki fase akhir yang sangat berbahaya atau yang kita ketahui sebagai AIDS. Keywords—HIV, machine learning, rapid miner
Breast Cancer Classification Using Outlier Detection and Variance Inflation Factor Budi Juarto
Engineering, MAthematics and Computer Science (EMACS) Journal Vol. 5 No. 1 (2023): EMACS
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v5i1.9223

Abstract

In terms of malignant tumors, breast cancer is one of the most prevalent. Breast cancer is a form of cancer that develops in the breast tissue when the surrounding, healthy breast tissue is overtaken by the uncontrollably growing cells in the breast tissue. Several features or patient conditions can be used in a machine learning approach to predict breast cancer. Machine learning will be utilized in these situations to determine if the cancer is malignant or benign. The Wisconsin Breast Cancer (Diagnostic) Data Set, which contains 32 characteristics and 569 collected data, was the dataset used in this research.. Feature selection in this study is done by eliminating outliers using the upper and lower quartile of each feature then feature selection is also carried out on features that have features that have a high variance inflation factor. The machine learning methods used in this research are Logistic Regression, Random Forest, KNN, SVC, XG Boost, Gradient Boosting, and Ridge Classifier. The selection of this method is based on the target that will be predicted by 2 labels, namely benign cancer, and malignant cancer. The result obtained is that the selection of features using the variance inflation factor increases the accuracy of the previous Logistic Regression and Random Forest methods from 98.25% to 99.12%. The method that has the highest level of accuracy is the Logistic Regression and Random Forest methods which have a value of 99.12%. The next research will be developed by trying other optimization techniques for hyperparameter tuning.
Mobile Deep Learning-Based Coffee Bean Quality Classification and Smartphone Integration Using Transfer Learning Budi Juarto; Yulianto Yulianto
Sisfo: Jurnal Ilmiah Sistem Informasi Vol. 10 No. 1 (2026): Sisfo: Jurnal Ilmiah Sistem Informasi, Mei 2026
Publisher : Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/sisfo.v10i1.27030

Abstract

Manually classifying good quality coffee beans is subjective, can take a considerable amount of time and is hard to standardize among various operators. For this research, computer vision was used to create a classifying system, dividing coffee bean pictures into defect and Good Quality. Based on mobile execution performance, we evaluated five existing computer vision transfer learning models. Image datasets include those used to create it, public online collections of coffee bean images, a primary set collected for use during research and the full combined 1,102-image collection broken down into a training (858 pictures), validation (114 pictures) and test (130 pictures) set. We made images of the same 224x224 resolution, then used an augmentation pipeline that rotated, flipped randomly horizontally and added color changes to increase robustness. Normalized the pixel intensities using statistics gathered for ImageNet. Models were trained identically and used Adam for optimizer and a learning rate, batch size and epoch quantity. Densely Connected Convolutional Neural Network (DenseNet121), EfficientNetB0, MobileNetV2, Residual Network (ResNet50) and Xception all performed at equal settings during experimentation. The top accuracy level came from EfficientNetB0 and Xception, both reaching 96.92% on the testing data. We selected EfficientNetB0 as our core model for its performance, small size and steady use on a smartphone (as seen in the application prototype we made), but it was still a solid performing alternative to Xception. The Android prototype that came from our study supported photo input using either a camera or file upload to provide instant quality status. Transfer learning's ability to enable the use of models capable of automated and consistent assessment for coffee bean quality control would be an improvement in small coffee operations.
Co-Authors Aden Wahyu P. Alfian R Putra Felix Indra Kurniadi Ika Dyah Agustia Rachmawati Risma Yulistiani Rizky A. Susanto Yulianto Yulianto Yulianto Yulianto