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All Journal International Journal of Electrical and Computer Engineering Jurnal Sistem Komputer Bulletin of Electrical Engineering and Informatics Jurnal Informatika Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Bulletin of Electrical Engineering and Informatics Telematika : Jurnal Informatika dan Teknologi Informasi Sinergi Jurnal Teknologi Informasi dan Ilmu Komputer JUITA : Jurnal Informatika International Journal of Advances in Intelligent Informatics Seminar Nasional Informatika (SEMNASIF) Register: Jurnal Ilmiah Teknologi Sistem Informasi JURNAL NASIONAL TEKNIK ELEKTRO Bulletin of Electrical Engineering and Informatics Jurnal Teknologi dan Sistem Komputer Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JIKO (Jurnal Informatika dan Komputer) Jurnal Sisfokom (Sistem Informasi dan Komputer) ILKOM Jurnal Ilmiah Compiler MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) GERVASI: Jurnal Pengabdian kepada Masyarakat Systemic: Information System and Informatics Journal Journal of Information Systems and Informatics Buletin Ilmiah Sarjana Teknik Elektro International Journal of Engineering, Technology and Natural Sciences (IJETS) Indonesian Journal of Electrical Engineering and Computer Science International Journal of Advances in Data and Information Systems Journal of Innovation Information Technology and Application (JINITA) Science in Information Technology Letters Masyarakat Berkarya: Jurnal Pengabdian dan Perubahan Sosial JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
Saifullah, Shoffan
Universitas Pembangunan Nasional Veteran Yogyakarta
Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Public Health Social Sciences Other

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PERBANDINGAN SEGMENTASI PADA CITRA ASLI DAN CITRA KOMPRESI WAVELET UNTUK IDENTIFIKASI TELUR Saifullah, Shoffan; Sunardi, Sunardi; Yudhana, Anton
ILKOM Jurnal Ilmiah Vol 8, No 3 (2016)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v8i3.75.190-196

Abstract

Citra digital merupakan gambaran yang jelas dari objek yang dapat diolah dengan komputer. Semakin besar ukuran (pixel) citra akan membutuhkan tempat penyimpanan yang besar pula. Dasar pengolahan citra yang dilakukan dalam penelitian ini terletak pada proses segmentasi pengolahan citra. Hal yang perlu dipertimbangkan adalah objek dari citra telur ayam yang akan diidentifikasi. Proses pengolahan citra melibatkan beberapa proses mulai dari akuisisi citra, preprocessing dan proses pengolahan citra sampai hasilnya. Preprocessing dilakukan untuk proses segmentasi yaitu dengan mengubah citra menjadi citra grayscale, dan kemudian diubah menjadi citra hitam putih. Dalam setiap proses dilakukan padding haar untuk mengurangi ukuran (size on disk) dengan matrik haar 8x8. Dan juga dilakukan proses dilasi dan opening untuk membuat objek terlihat jelas serta menghaluskan permukaan untuk menghilangkan noise. Pada proses pengolahannya dilakukan dengan menggunakan segmentasi dan pelabelan dengan didahului dengan perhitungan centroid dan penentuan bounding box untuk mengidentifikasi telur ayam. Perbandingan hasil pengolahan citra asli dengan hasil kompresi dari citra asli menunjukkan bahwa proses segmentasi citra telur ayam memberikan hasil 100% sama (baik citra asli maupun citra kompresi wavelet). Dengan kompresi akan menghemat penyimpanan (disk) dan hasil yang sama diperoleh dalam proses perhitungan objek, luas area, dan penentuan titik centroid.
Identification of chicken egg fertility using SVM classifier based on first-order statistical feature extraction Saifullah, Shoffan; Suryotomo, Andiko Putro
ILKOM Jurnal Ilmiah Vol 13, No 3 (2021)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v13i3.937.285-293

Abstract

This study aims to identify chicken eggs fertility using the support vector machine (SVM) classifier method. The classification basis used the first-order statistical (FOS) parameters as feature extraction in the identification process. This research was developed based on the processs identification process, which is still manual (conventional). Although currently there are many technologies in the identification process, they still need development. Thus, this research is one of the developments in the field of image processing technology. The sample data uses datasets from previous studies with a total of 100 egg images. The egg object in the image is a single object. From these data, the classification of each fertile and infertile egg is 50 image data. Chicken egg image data became input in image processing, with the initial process is segmentation. This initial segmentation aims to get the cropped image according to the object. The cropped image is repaired using image preprocessing with grayscaling and image enhancement methods. This method (image enhancement) used two combination methods: contrast limited adaptive histogram equalization (CLAHE) and histogram equalization (HE). The improved image becomes the input for feature extraction using the FOS method. The FOS uses five parameters, namely mean, entropy, variance, skewness, and kurtosis. The five parameters entered into the SVM classifier method to identify the fertility of chicken eggs. The results of these experiments, the method proposed in the identification process has a success percentage of 84.57%. Thus, the implementation of this method can be used as a reference for future research improvements. In addition, it may be possible to use a second-order feature extraction method to improve its accuracy and improve supervised learning for classification.
Performance Analysis of IoT-Based pH and EC Control System for Smart Indoor Hydroponics Pagoda Mustard Cultivation Bagus Muhammad Akbar; Shoffan Saifullah; Arif Umami
Journal of Innovation Information Technology and Application (JINITA) Vol 8 No 1 (2026): JINITA, June 2026
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/jinita.v8i1.3080

Abstract

Indoor hydroponic systems increasingly utilize IoT technologies for automated nutrient management, yet empirical performance data from extended operational trials remain scarce. This study quantifies control system performance and biological validation for an IoT-based pH-EC control implementation during 35-day continuous operation. The system employed ESP32 microcontroller with safety-based threshold control (pH <8.2, TDS >900 ppm) optimized for reliability rather than precision in a 30L Deep Flow Technique system cultivating 14 pagoda mustard plants. Analysis of 12,219 datapoints revealed TDS control achieved 99.3% time-in-safe-range (MAE 50.06 ppm, settling time 671.6 min) while pH control maintained MAE 0.74 units (settling time 1266.9 min) without exceeding critical thresholds. Plant outcomes validated control effectiveness: final height 20.59 ± 2.03 cm, leaf count 35.50 ± 11.83, with consistent SPAD values >40 indicating adequate nitrogen nutrition despite sub-optimal TDS levels (867 ppm vs. optimal 1050-1400 ppm). Results demonstrate that simplified rule-based control achieves satisfactory biological outcomes when safety thresholds prevent metabolic stress, contributing validated operational benchmarks for resource-constrained hydroponic applications.
K-Means Segmentation Based-on Lab Color Space for Embryo Detection in Incubated Egg Shoffan Saifullah; Rafal Drezewski; Alin Khaliduzzaman; Lean Karlo Tolentino; Rabbimov Ilyos
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 8 No. 2 (2022): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v8i2.23724

Abstract

The quality of the hatching process influences the success of the hatch rate besides the inherent egg factors. Eliminating infertile or dead eggs and monitoring embryonic growth are very important factors in efficient hatchery practices. This process aims to sort eggs that only have embryos to remain in the incubator until the end of the hatching process. This process aims to sort eggs with embryos to remain hatched until the end. Maximum checking is done the first week in the hatching period. This study aims to detect the presence of embryos in eggs. Detection of the existence of embryos is processed using segmentation. Egg images are segmented using the K-means algorithm based on Lab color images. The results of the image acquisition are converted into Lab color space images. The results of Lab color space images are processed using K-means for each color. The K-means process uses cluster k=3, where this cluster divides the image into three parts: background, eggs, and yolk. Egg yolks are part of eggs that have embryonic characteristics. This study applies the concept of color in the initial segmentation and grayscale in the final stages. The initial phase results show that the image segmentation results using k-means clustering based on Lab color space provide a grouping of three parts. At the grayscale image processing stage, the results of color image segmentation are processed with grayscaling, image enhancement, and morphology. Thus, it seems clear that the yolk segmented shows the presence of egg embryos. Based on this process and results, the initial stages of the embryo detection process used K-means segmentation based on Lab color space. The evaluation uses MSE and MSSIM, with values of 0.0486 and 0.9979; this can be used as a reference that the results obtained can detect embryos in egg yolk. This protocol could be used in a non-destructive quantitative study on embryos and their morphology in a precision poultry production system in the future.
Nondestructive Chicken Egg Fertility Detection Using CNN-Transfer Learning Algorithms Shoffan Saifullah; Rafal Drezewski; Anton Yudhana; Andri Pranolo; Wilis Kaswijanti; Andiko Putro Suryotomo; Seno Aji Putra; Alin Khaliduzzaman; Anton Satria Prabuwono; Nathalie Japkowicz
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 9 No. 3 (2023): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i3.26722

Abstract

This study explores the application of CNN-Transfer Learning for nondestructive chicken egg fertility detection. Four models, VGG16, ResNet50, InceptionNet, and MobileNet, were trained and evaluated on a dataset using augmented images. The training results demonstrated that all models achieved high accuracy, indicating their ability to accurately learn and classify chicken eggs’ fertility state. However, when evaluated on the testing set, variations in accuracy and performance were observed. VGG16 achieved a high accuracy of 0.9803 on the testing set but had challenges in accurately detecting fertile eggs, as indicated by a NaN sensitivity value. ResNet50 also achieved an accuracy of 0.98 but struggled to identify fertile and non-fertile eggs, as suggested by NaN values for sensitivity and specificity. However, InceptionNet demonstrated excellent performance, with an accuracy of 0.9804, a sensitivity of 1 for detecting fertile eggs, and a specificity of 0.9615 for identifying non-fertile eggs. MobileNet achieved an accuracy of 0.9804 on the testing set; however, it faced challenges in accurately classifying the fertility status of chicken eggs, as indicated by NaN values for both sensitivity and specificity. While the models showed promise during training, variations in accuracy and performance were observed during testing. InceptionNet exhibited the best overall performance, accurately classifying fertile and non-fertile eggs. Further optimization and fine-tuning of the models are necessary to address the limitations in accurately detecting fertile and non-fertile eggs. This study highlights the potential of CNN-Transfer Learning for nondestructive fertility detection and emphasizes the need for further research to enhance the models’ capabilities and ensure accurate classification.
Prediction of palm oil production using hybrid decision tree based on fuzzy inference system Tsukamoto Tundo Tundo; Shoffan Saifullah; Mesra Betty Yel; Opi Irawansah; Zulfikar Yusya Mubarak; Andi Saidah
Bulletin of Electrical Engineering and Informatics Vol 13, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i6.7773

Abstract

This research addresses the challenge of optimizing rule creation for palm oil production at PT Tapiana Nadenggan. It deals with the complexity of diverse agricultural variables, environmental factors, and the dynamic nature of palm oil production. The existing problem lies in the limitations of conventional decision tree models—J48, reduced error pruning (REP), and random—in capturing the nuanced relationships within the intricate palm oil production system. The study introduces hybrid decision tree models—specifically J48-REP, REP-Random, and Random-J48—to address this challenge via combination scenarios. This approach aims to refine and update the rule creation process, enabling the recognition of nuanced performance processes within the selected decision tree combinations. To comprehensively tackle this challenge and problem, the study employs Tsukamoto’s fuzzy inference system (FIS) for a sophisticated performance comparison. Despite the complexity, intriguing results emerge after the forecasting process, with the standalone J48 decision tree achieving 85.70% accuracy and the combined J48-REP excelling at 93.87%. This highlights the potential of decision tree combinations in overcoming the complexities inherent in forecasting palm oil production, contributing valuable insights for informed decision-making in the industry.
Co-Authors Abdul Fadlil Adityo Nugroho, Adityo Afiqa, Nurul Agus Sasmito Aribowo Ahmad Taufiq Akbar Ahmad Tri Hidayat Aji Prasetya Wibawa Akbar, Bagus Muhammad Alek Setiyo Nugroho Alfiani, Oktavia Dewi Alin Khaliduzzaman Alin Khaliduzzaman Alisya Amalia Putri Hasanah Andi Muhammad Dirham Dewantara Andiko Putro Suryotomo Andri Pranolo Anton Satria Prabuwono Anton Yudhana Arianti, Berliana Andra Arief Hermawan Arif - Umami Awang Hendrianto Pratomo Azlan, Faris Farhan Azrul Mahfurdz Bambang Yuwono Betty Yel, Mesra Budi Santosa Devia, Elmi Dharmawan, Tio Dreżewski, RafaÅ‚ Drezewski, Rafal Drezewski, Rafał Dwi Wahyuningrum Dwiyanto, Felix Andika Faqihuddin Al-anshori Ghazali, Ahmad Badaruddin Haekal, Haekal Herlina Jayadianti Heru Cahya Rustamaji Hidayat, Ahmad Tri Humairoh, Nanda Lailatul Ismail, Amelia Ritahani Isna Nur Aini Ivana Puspita Sari Judanti Cahyaning Junaidi Junaidi Kusuma, M. Apriandi Lean Karlo Tolentino Luh Putu Ratna Sundari Muhammad Nur Hendra Alvianto Nathalie Japkowicz Nisa, Syed Qamrun Noormaizan, Khairul Akmal Nur Heri Cahyana Nuril Anwar, Nuril Nuryana, Zalik Opi Irawansah Prapcoyo, Hari Putra, Agung Bella Utama Rabbimov Ilyos Rabbimov, Ilyos Rafal Drezewski Rafal Drezewski Rafal Drezewski Rochmat Husaini Rochmat Husaini Rustamadji, Heru Saidah, Andi Santosa, Budi Satya Ghifari Adipratama Seno Aji Putra Suhirman SUHIRMAN SUHIRMAN Sularso Sularso, Sularso Sunardi - Sunardi - Sunardi Sunardi Sunardi, Sunardi Taufiq Akbar, Ahmad Tri Andi, Tri Tundo Tundo Tundo, Tundo Tuti Purwaningsih, Tuti Wahyu Adjie Saputra Wilis Kaswidjanti Wilis Kaswidjanti Wilis Kaswijanti Yuhefizar Yuhefizar Yuli Fauziah Yuli Fauziyah Zulfikar Yusya Mubarak