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Yana Aditia Gerhana, Yana Aditia
Teknik Informatika Fakultas Sains dan Teknologi UIN Sunan Gunung Djati Bandung
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Library & Information Science Social Sciences

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Price Prediction of Second-Hand Iphones Using Random Forest Regression Based on Unit Conditions Anggayana, Denta Pratama; Taufik, Ichsan; Gerhana, Yana Aditia
ISTEK Vol. 14 No. 1 (2025)
Publisher : Fakultas Sains dan Teknologi UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/istek.v14i1.2154

Abstract

This study presents the development of a price prediction model for second-hand Iphones based on unit conditions using the Random Forest Regression algorithm, implemented in a web-based application. A dataset of 542 records was collected from Facebook Marketplace and iPhone trading groups, with variables including Iphone type, storage capacity, warranty status, Face ID, and Truetone. The research employed the CRISP-DM methodology through the stages of business understanding, data understanding, data preparation, modeling, evaluation, and deployment. The model was tested using data splits of 80%–20%, 70%–30%, and 60%–40%, resulting in MAE values of 8.32%–8.42% and RMSE values of 10.64%–10.88%, indicating good and consistent accuracy. The developed system can automatically provide price recommendations based on unit conditions, assisting both sellers and buyers in determining fair market prices.
Implementasi Teknologi Augmented Reality pada Buku Panduan Wudhu Berbasis Mobile Android Setiawan, Erwin; Syaripudin, Undang; Gerhana, Yana Aditia
JOIN (Jurnal Online Informatika) Vol 1 No 1 (2016)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v1i1.8

Abstract

Augmented Reality (AR) adalah teknologi interaktif yang mampu memproyeksikan objek maya ke dalam objek nyata secara real time. Perkembangan teknologi AR dewasa ini telah memberikan banyak kontribusi ke dalam berbagai bidang. Salah satu implementasi AR di bidang edukasi adalah AR Book. Buku merupakan salah satu media pembelajaran yang banyak digunakan. Selain itu, buku juga digunakan sebagai alat berkomunikasi oleh guru maupun orang tua terhadap anak-anak, misalkan seperti jenis buku panduan mengenai tatacara wudhu. Wudhu adalah suatu bentuk peribadatan kepada Allah Ta’ala dengan mencuci anggota tubuh tertentu dengan tata cara yang khusus. Wudhu khususnya diperintahkan sebelum melaksanakan ibadah shalat dan thawaf. Umat muslim harus mengetahui tatacara berwudhu yang benar. Salah satu sistem operasi yang digunakan pada mobile phone atau smartphone yaitu Android. Android adalah sebuah sistem operasi untuk perangkat mobile yang berbasis Linux dan bersifat open source. Dengan memanfaatkan media mobile untuk membangun aplikasi menggunakan teknologi augmented reality sebagai media pembelajaran, aplikasi AR berbasis mobile mempunyai keunggulan karena sifatnya yang mudah berpindah
Comparison of Template Matching Algorithm and Feature Extraction Algorithm in Sundanese Script Transliteration Application using Optical Character Recognition Gerhana, Yana Aditia; Atmadja, Aldy Rialdy; Padilah, Muhamad Farid
JOIN (Jurnal Online Informatika) Vol 5 No 1 (2020)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v5i1.580

Abstract

The phenomenon that occurs in the area of West Java Province is that the people do not preserve their culture, especially regional literature, namely Sundanese script, in this digital era there is research on Sundanese script combined with applications using Feature Extraction algorithm, but there is no comparison with other algorithms and cannot recognize Sundanese numbers. Therefore, to develop the research a Sundanese script application was made with the implementation of OCR (Optical Character Recognition) using the Template Matching algorithm and the Feature Extraction algorithm that was modified with the pre-processing stages including using luminosity and thresholding algorithms, from the two algorithms compared to the accuracy and time values the process of recognizing digital writing and handwriting, the results of testing digital writing algorithm Matching algorithm has a value of 87% word recognition accuracy with 236 ms processing time and 97.6% character recognition accuracy with 227 ms processing time, Feature Extraction has 98% word recognition accuracy with 73.6 ms processing time and 100% character recognition accuracy with 66 ms processing time, for handwriting recognition in feature extraction character recognition has 83% accuracy and 75% word recognition , while template matching in character recognition has an accuracy of 70% and word recognition has an accuracy of 66%.
Chatbot for Signaling Quranic Verses Science Using Support Vector Machine Algorithm Syaripudin, Undang; Suparman, Deden; Gerhana, Yana Aditia; Rahayu, Ayu Puji; Mintarsih, Mimin; Alawiyah, Rizka
JOIN (Jurnal Online Informatika) Vol 6 No 2 (2021)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v6i2.827

Abstract

The many verses in the Qur'an encourage finding the right way how to understand it thematically. The purpose of the research is to develop a chatbot application that can be used to explore and elaborate the content of verses in the Qur’an that hint at science. The support vector machine (SVM) algorithm classifies question and answers datasets in chatbot applications. The number of data sets used is 76, with test data as much as 10%. The test results show that the SVM algorithm is quite good in classifying, with an accuracy value of 87.5%. While the user test results obtained an average MOS of 8.4, which means the chatbot application developed is very effective in understanding the Qur'an, which implies science. This research is expected to provide an overview of the explanation of the Qur'an about science and technology.
Automatic Detection of Hijaiyah Letters Pronunciation using Convolutional Neural Network Algorithm Gerhana, Yana Aditia; Azis, Aaz Muhammad Hafidz; Ramdania, Diena Rauda; Dzulfikar, Wildan Budiawan; Atmadja, Aldy Rialdy; Suparman, Deden; Rahayu, Ayu Puji
JOIN (Jurnal Online Informatika) Vol 7 No 1 (2022)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v7i1.882

Abstract

Abstract— Speech recognition technology is used in learning to read letters in the Qur'an. This study aims to implement the CNN algorithm in recognizing the results of introducing the pronunciation of the hijaiyah letters. The pronunciation sound is extracted using the Mel-frequency cepstral coefficients (MFCC) model and then classified using a deep learning model with the CNN algorithm. This system was developed using the CRISP-DM model. Based on the results of testing 616 voice data of 28 hijaiyah letters, the best value was obtained for accuracy of 62.45%, precision of 75%, recall of 50% and f1-score of 58%.
Digital Image Processing Using YCbCr Colour Space and Neuro Fuzzy to Identify Pornography Subaeki, Beki; Gerhana, Yana Aditia; Rusyana, Meta Barokatul Karomah; Manaf, Khaerul
JOIN (Jurnal Online Informatika) Vol 8 No 1 (2023)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v8i1.1070

Abstract

Pornography is a severe problem in Indonesia, apart from drugs. This can be seen based on data from the Ministry of Communication and Informatics in 2021 which found 1.1 million pornographic content online. The increasing number of access to pornographic content sites on the internet can prove this. Several studies have been conducted to produce preventive formulas. However, this research flow has not been effective in solving the problem. This is because the results of the identification value in the output image obtained are not quite right. This study proposes a procedure for identifying pornographic content in digital images as an alternative approach for the early stages of a destructive content access prevention system. The formulation uses the YCbCr color space to analyze human skin on image objects that represent exposed body parts and the classification process with the Neuro Fuzzy approach. The performance of this formula was tested on 100 digital images of random categories of human objects (usually covered, skimpy, and naked) taken from the internet. The test results are at a relatively good level of accuracy, with a weight of 70% for the entire test data.
Deteksi Generatif Teks pada Penilaian Otomatis Tes Esai Berbahasa Indonesia Menggunakan IndoBERT Pitriani, Pitriani; Maylawati, Dian Sa’adillah; Gerhana, Yana Aditia
JEPIN (Jurnal Edukasi dan Penelitian Informatika) Vol 11, No 2 (2025): Volume 11 No 2
Publisher : Program Studi Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/jp.v11i2.93221

Abstract

Hadirnya model generative artificial intelligence (GenAI) membawa tantangan baru dalam dunia pendidikan, khususnya terkait integritas. Salah satu isu yang mencuat adalah potensi penggunaan teks yang dihasil GenAI dalam jawaban pada proses penilaian pembelajaran peserta didik. Oleh karena itu, penelitian ini bertujuan untuk mendeteksi generatif teks hasil perangkat AI pada penilaian otomatis evaluasi pembelajaran dalam bentuk esai dengan bahasa Indonesia. Metode penelitian yang digunakan mengadaptasi model pre-trained Indonesia Bidirectional Encoder Representations from Transformers (IndoBERT). IndoBERT digunakan untuk deteksi generatif teks dengan AI melalui fine-tuning dan penilaian esai otomatis dengan representasi embedding dan cosine similarity dengan mempertimbangkan hasil deteksi GenAI. Hasil eksperimen menunjukkan fine-tuning pada model pre-trained IndoBERT berhasil mencapai akurasi sebesar 93.91% dengan nilai validation loss sebesar sebesar 0.1895. Sementara itu, pada tahap integrasi model deteksi teks GenAI ke dalam penilaian otomatis menunjukkan bahwa deteksi teks GenAI dapat mempengaruhi nilai akhir, khususnya pada jawaban yang memiliki similaritas tinggi dengan kunci jawaban namun terindikasi AI.
Implementation of Rule-Base and Internet Methods of Things Optimizing Water Mangement For Improving Seed Quality Gerhana, Yana Aditia; Suparman, Deden
ISTEK Vol. 13 No. 1 (2024): Juni 2024
Publisher : Fakultas Sains dan Teknologi UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/istek.v13i1.930

Abstract

System hydroponics Nutrients Film Technique (NFT) is one of the increasingly popular plant cultivation techniques used because it can increase the efficiency of water and nutrient use as well as crop yields. The NFT Hydroponic System has problems that are often faced in the form of control that must be optimal for important parameters like pH, temperature water, And concentration nutrition, so that can influence plant health and growth and need a good environment controlled To avoid decline quality plant or withering plant. Study This design uses Arduino Uno as a center control system monitoring hydroponics NFTs Which in add sensors pH For read value from pH water, sensors TDS used For read density nutrition, sensors temperature DS18B20 used For read temperature water Because own waterproff and water sensor features flow to read the amount of water flow. Data is read by the sensor and Then sent to Firebase through module NodeMCU which has been connected to the Arduino Uno then from Firebase it is created output form information to the user through the application mobile. Results testing done with the use 3 media Which were different as much 60 time experienced 58 successes and 2 failures resulted in a score accuracy of 96.6% of the total testing.
Implementation of Convolutional Neural Network CNN Algorithm to Detect Coffe Fruit Maturity Gerhana, Yana Aditia; Heryanto, Rafi Rai; Syaripudin, Undang; Suparman, Deden
ISTEK Vol. 13 No. 2 (2024): Desember 2024
Publisher : Fakultas Sains dan Teknologi UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/istek.v13i2.1247

Abstract

Fruit ripeness detection is important in the agriculture and food processing industries to ensure optimal product quality. Proper fruit ripeness can affect flavour, texture and nutrition, making it a key focus in production process monitoring and control. The fruit ripeness detection process still needs to be done manually, which can be inefficient and inaccurate. This research aims to address these challenges by implementing the CNN algorithm with VGG-19 architecture to detect coffee fruit ripeness automatically. The process involves collecting datasets of fruit images with various ripeness levels, image pre-processing including cropping and resizing, training the CNN VGG-19 model with feature learning and hyperparameter optimisation and evaluating model performance using a confusion matrix. This experiment aims to evaluate the model's performance in detecting fruit ripeness and measure the speed and efficiency of the CNN-based detection system with VGG-19 architecture. The results of this research are expected to help develop a better system for identifying fruit ripeness.
Co-Authors Agung Wahana ALAWIYAH, RIZKA Aldy Rialdy Atmadja Anggayana, Denta Pratama Anwar, Muhammad Afian Arief Fatchul Huda Aulia, Salman Fauzan Fahri Ayu Puji Rahayu Azis, Aaz Muhammad Hafidz Beki Subaeki, Fatkhan Gunawan, Aldy Rialdy Atmadja, Beki Cecep Nurul Alam Cepy Slamet Deden Suparman, Deden Devi Novitasari Dhanis Al Ghifari Diena Rauda Ramdania Dikdik Firman Sidik Dzulfikar, Wildan Budiawan Erwin Setiawan, Erwin Faiz M. Kaffah Firdaus, Muhammad Deden Heryanto, Rafi Rai Ichsan Taufik, Ichsan Khaerul Manaf Kusuma Pradana, Galih Maylawati, Dian Sa'adillah Mintarsih, Mimin Muhammad Ali Ramdhani Muharam, Aldi Fahluzi Nur Lukman Nurlatifah, Eva Padilah, Muhamad Farid Pitriani, Pitriani Qomaruddin, Nurhadi Rahman, Titik Khawa Abdul Rusyana, Meta Barokatul Karomah Somantri, E. Aris Taufik, Ichsan Undang Syaripudin, Undang