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Haris Suhendar
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Electrical & Electronics Engineering Energy Physics Other

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OPTIMIZATION OF TCS3200 COLOR SENSOR UTILIZATION IN THE DEVELOPMENT OF AN AUTOMATED SYSTEM FOR COFFEE BEAN SORTING BASED ON DISTANCE DIFFERENCES: OPTIMISASI PEMANFAATAN SENSOR WARNA TCS3200 DALAM PENGEMBANGAN SISTEM OTOMATIS UNTUK PEMILAHAN BIJI KOPI BERDASARKAN PERBEDAAN JARAK Hanum Puji Pangesti; Hadi Nasbey; Haris Suhendar
PROSIDING SEMINAR NASIONAL FISIKA (E-JOURNAL) Vol. 13 (2025): PROSIDING SEMINAR NASIONAL FISIKA (E-JOURNAL) SNF2024
Publisher : Program Studi Pendidikan Fisika dan Program Studi Fisika Universitas Negeri Jakarta, LPPM Universitas Negeri Jakarta, HFI Jakarta, HFI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/03.1301.FA07

Abstract

The growing demand for high-quality coffee beans has driven the development of automated systems for coffee bean sorting. A critical component in such systems is the color sensor, which provides accurate information to optimize its usage in coffee bean sorting. This study tested the TCS3200 color sensor to optimize its application in the design of an automated coffee bean sorting system based on distance variations. The methodology involved identifying parameters affecting measurement accuracy and adjusting sensor parameters to respond to variations in color using three different colored papers. The sensor's distance was tested within a range of 1 cm to 20 cm, with increments of 0.5 cm. The results indicated that the optimal sensor distances were 16.5 cm, 7.5 cm, and 15 cm for the three different object colors, with an object size of 14.8 cm x 21 cm. These findings will be applied to the development of an automated coffee bean sorting system using Arduino Uno and the TCS3200 color sensor.
ANALYSIS OF COCONUT MATURITY LEVEL RECOGNITION USING PRINCIPAL COMPONENT ANALYSIS (PCA) METHOD BASED ON COCONUT KNOCKING SOUND DATA IN OPEN SPACES: ANALISIS PENGENALAN TINGKAT KEMATANGAN KELAPA MENGGUNAKAN METODE PRINCIPAL COMPONENT ANALYSIS (PCA) BERDASARKAN DATA SUARA KETUKAN KELAPA DI RUANG TERBUKA Yusuf Niko Fitranto; Bambang Heru Iswanto; Haris Suhendar
PROSIDING SEMINAR NASIONAL FISIKA (E-JOURNAL) Vol. 13 (2025): PROSIDING SEMINAR NASIONAL FISIKA (E-JOURNAL) SNF2024
Publisher : Program Studi Pendidikan Fisika dan Program Studi Fisika Universitas Negeri Jakarta, LPPM Universitas Negeri Jakarta, HFI Jakarta, HFI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/03.1301.FA09

Abstract

Coconuts delivered from farmers generally have different variations in maturity. This study aims to identify the maturity of coconut fruit based on acoustic feature analysis using the Principal Component Analysis (PCA) method. This method combines data acquisition of coconut tapping sounds with multivariate statistical analysis to acoustically recognise the type of coconut maturity. The research experiment involved 40 coconut samples and the recording of coconut knocking sound was done by knocking the coconut three times from each coconut sample using a coconut knocking device in an open space. Time and frequency domain acoustic features were extracted from the resulting audio signals. Subsequently, PCA analysis was used to reduce the dimensionality of the acoustic features and identify patterns that represent the ripeness level of the coconut. The results of PCA visualisation obtained differences in acoustic features from young and old coconut maturity levels can be identified. From the results of analysis using PCA, the first two principal components explained about 40.28% and the second principal component explained about 30.07% of the data variation. Visualisation of the data using a scree plot shows that the young coconut group is clearly separated from the old coconut group.
CLASSIFICATION OF CHICKEN EGG SHELL QUALITY USING EFFICIENTNET BASED ON DIGITAL IMAGES: KLASIFIKASI KUALITAS CANGKANG TELUR AYAM MENGGUNAKAN EFFICIENTNET BERBASIS CITRA DIGITAL Hernanda Khoiriyah Putri; Bambang Heru Iswanto; Haris Suhendar
PROSIDING SEMINAR NASIONAL FISIKA (E-JOURNAL) Vol. 13 (2025): PROSIDING SEMINAR NASIONAL FISIKA (E-JOURNAL) SNF2024
Publisher : Program Studi Pendidikan Fisika dan Program Studi Fisika Universitas Negeri Jakarta, LPPM Universitas Negeri Jakarta, HFI Jakarta, HFI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/03.1301.FA13

Abstract

Cracks in eggshells often occur during the distribution process, both visible and invisible to the naked eye. Cracks in eggshells are a serious concern as they can lead to contamination and health risks for consumers. This study classifies cracks in chicken eggshells based on digital images using a Convolutional Neural Network (CNN)-EfficientNet. The experiment was conducted with a sample of 300 egg images in three conditions: good, cracked, and broken, with 100 images for each condition. The images were captured using a calibrated DSLR camera with a stable background. Data preprocessing included cropping, resizing, and augmentation. The data was split in an 80:20 ratio. Hyperparameters used the Adam optimizer with 50 iterations and a batch size of 32. Model performance was evaluated using loss function metrics (sparse categorical crossentropy), accuracy, and confusion matrix. Classification using EfficientNet-B0 to B3 resulted in accuracy, precision, recall, and F1-Score of 94.52%, 95.75%, 95.71%, and 95.73%; 94.05%, 94.09%, 94.05%, and 94.02%; 94.52%, 94.56%, 94.52%, and 94.54%; and 97.14%, 97.19%, 97.14%, and 97.15%, respectively. Based on the results, classification using EfficientNet shows improved performance as the model complexity increases. The findings suggest that images of eggshell cracks can be utilized for egg quality identification and can be developed for chicken egg quality classification.
Co-Authors Bambang Heru Iswanto Delila Septiani Dwi Putri Hadi Nasbey Hanum Puji Pangesti Hernanda Khoiriyah Putri Jhaniya Zahra Nawawi Muhammad Rizky Anugrah Rian Setiyana Siska Miati Jati Ningsih Teguh Budi Prayitno Yusuf Niko Fitranto