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All Journal Jurnal Riset Informatika eProceedings of Engineering
Paryasto , Marisa W.
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering

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Komparasi Hasil Prediksi Dengan Menggunakan Hyperparameter Tuning Antara Random Search dan Grid Search Fazril, Ibnu; Prasasti , Anggunmeka Luhur; Paryasto , Marisa W.
eProceedings of Engineering Vol. 12 No. 5 (2025): Oktober 2025
Publisher : eProceedings of Engineering

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Abstract

Abstrak — Peningkatan yang signifikan pada transaksikeuangan mencurigakan yang berpotensi merugikan lembagakeuangan dan masyarakat semakin luas. Pencucian uang danpenipuan finansial merupakan ancaman serius yang sulitdideteksi oleh sistem tradisional, yang sering kali tidak mampumengimbangi kompleksitas metode kriminal yang semakincanggih. Masalah utama penelitian ini adalah bagaimanameningkatkan akurasi dan efisiensi dalam mendeteksitransaksi mencurigakan menggunakan teknologi MachineLearning. Penelitian ini dilakukan dengan mengembangkanmodel pendeteksi transaksi mencurigakan menggunakanalgoritma XGBoost, Decision Tree, dan Logistic Regressiondengan membandingkan pencarian parameter terbaik untukHyperparameter Tuning antara Random Search dan GridSearch dalam menghasilkan prediksi yang bernilai tinggi.Kata kunci— fraud, xgboost, decision tree, logisticregression, random search, grid search, hyperparameter tuning
ALEXNET ARCHITECTURE AND FUZZY ANALYSIS ON TALENT JUDGE DECISION PREDICTION BASED ON FACIAL EXPRESSION Zaki, Muhammad; Prasasti , Anggunmeka Luhur; Paryasto , Marisa W.
Jurnal Riset Informatika Vol. 4 No. 4 (2022): September 2022
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (706.456 KB) | DOI: 10.34288/jri.v4i4.171

Abstract

The expression on the human face is a means of non-verbal communication. In the talent search event, the facial expressions shown by the judges when watching the participants’ performances became one of the components to see whether the contestant who was performing could qualify for the next round or he would fail. Haar cascade is used to provide the location of the face in the frame and to classify the expressions on the face, a CNN model with modified AlexNet architecture is used which increases the accuracy by 5% from the original alexnet. A fuzzy Algorithm is used to predict the judge’s decision based on how many facial expressions appear during the participant’s appearance. The decision prediction system for talent search judges based on facial expressions using fuzzy is considered effective in predicting decisions, after being tested the system can predict decisions with an accuracy rate of 83%.
RESTAURANT DENSITY PREDICTION SYSTEM USING FEED FORWARD NEURAL NETWORK Sandi , Muhammad Kurnia; Prasasti , Anggunmeka Luhur; Paryasto , Marisa W.
Jurnal Riset Informatika Vol. 3 No. 2 (2021): March 2021 Edition
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v3i2.58

Abstract

In this day and age, information about something is so important. The level of trust of modern society depends on the testing of information. Tested and accurate information will have a good impact on the community. One of the important but often missed information is information about the density of a restaurant. Information about restaurant density is important to know because it can affect the actions of someone who will visit the restaurant. This information is also useful to provide information in advance so that diners avoid full restaurants to avoid the spread of the Covid-19 virus, among other things. With limited operating hours as well as the number of restaurant visitors, information about the density of a restaurant becomes much needed. The lack of information on restaurant density is a major problem in the community. The needs of the community, made this study aims to predict the density of a restaurant an hour later. Based on survey data and existing literature data, with simulation methods and also system analysis built using feedforward neural network artificial intelligence architecture and then trained with Backpropagation algorithms produced an accuracy of 97.8% with literature data.
Co-Authors ANGGUNMEKA LUHUR PRASASTI Fazril, Ibnu Muhammad Zaki Sandi , Muhammad Kurnia