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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Ta´dib JURNAL ISTEK SMATIKA JOIN (Jurnal Online Informatika) ISTEK
Undang Syaripudin, Undang
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Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Education Electrical & Electronics Engineering

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Comparison of search algorithms in Javanese-Indonesian dictionary application Yana Aditia Gerhana; Nur Lukman; Arief Fatchul Huda; Cecep Nurul Alam; Undang Syaripudin; Devi Novitasari
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 18, No 5: October 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v18i5.14882

Abstract

This study aims to compare the performance of Boyer-Moore, Knuth morris pratt, and Horspool algorithms in searching for the meaning of words in the Java-Indonesian dictionary search application in terms of accuracy and processing time. Performance Testing is used to test the performance of algorithm implementations in applications. The test results show that the Boyer Moore and Knuth Morris Pratt algorithms have an accuracy rate of 100%, and the Horspool algorithm 85.3%. While the processing time, Knuth Morris Pratt algorithm has the highest average speed level of 25ms, Horspool 39.9 ms, while the average speed of the Boyer Moore algorithm is 44.2 ms. While the complexity test results, the Boyer Moore algorithm has an overall number of n 26n2, Knuth Morris Pratt and Horspool 20n2 each.
PERANGKAT LUNAK BANTU PENGANALISAAN PENURUNAN MUTU AIR MELALUI TEKNIK CHLORINASI MENGGUNAKAN METODE LVQ (Studi Kasus PDAM Kabupaten Bekasi) Undang Syaripudin; Yogi Saputra
JURNAL ISTEK Vol 10, No 2 (2017): ISTEK
Publisher : JURNAL ISTEK

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Abstract

Penelelitian bertujuan untuk mengetahui tingkat penurunan mutu air melalui teknnik chlorinasi yang menggunakan metode LVQ. Metode penelitian yang digunakan adalah metode survey. Subjek penelitian ini adalah sebuah capture air yang sudah melalui proses chlorinasi di PDAM. Hasil penelitian menunjukan baik ditinjau dari faktor internal maupun faktor eksternal, penganalisaan penurunan kualitas mutu air melalui 4 unsur mutu air CO (Na dan Mn), DO, COD, dan BOD, melalui teknik chlorinasi, dengan rerata nilai koofisien korelasi dengan mengambil 15 data uji citra air yaitu sebesar 73 % sistem berhasil menampilkan informasi yang benar.
STUDI KOMPARATIF PENERAPAN METODE HIERARCHICAL, K-MEANS DAN SELF ORGANIZING MAPS (SOM) CLUSTERING PADA BASIS DATA Undang Syaripudin; Ijang Badruzaman; Erwan Yani; Dede K; M. Ramdhani
JURNAL ISTEK Vol 7, No 1 (2013): ISTEK
Publisher : JURNAL ISTEK

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Abstract

This study identifies the results of some test results clustering methods. The data set used in this test method Clustering. The third method of clustering based on these factors than the size of the data set and the extent of the cluster. The test results showed that the SOM algorithm produces better accuracy in classifying objects into matching groups. K-means algorithm is very good when using large data sets and compared with Hierarchical SOM algorithm. Hierarchical grouping and SOM showed good results when using small data sets compared to using k-means algorithm.
Decision Support System for Employee Recruitment Using El Chinix Traduisant La Realite (Electre) And Weighted Product (WP) Mohamad Irfan; Undang Syaripudin; Cecep Nurul Alam; Muhammad Hamdani
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.v5i1.606

Abstract

Management of human resources (HR) is important to achieve company goals. One of the activities in HR management is recruitment, selection, and training. Recruitment and selection are usually done not using a system so that the calculations are still done manually. But by processing data using the system can produce a decision in recommending prospective employees that can have a positive impact on the company. The company selection process is carried out through two stages: administrative selection and final selection in the form of psychological test assessment, interviews, ability tests and communication. The use of the Elimination Et Choix Traduisant La Realite (ELECTRE) method in the administrative selection stage and the Weighted Product (WP) method in the final selection stage is a new discovery made to get the best decision in accordance with the required criteria. By using this method the final results will be obtained namely the recommendation of several prospective employees who are fit to work in the company. The performance results of this system reach one hundred percent, the data from the system is in accordance with the expected calculation.
Decision Support System for Employee Recruitment Using El Chinix Traduisant La Realite (Electre) And Weighted Product (WP) Mohamad Irfan; Undang Syaripudin; Cecep Nurul Alam; Muhammad Hamdani
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.v5i1.606

Abstract

Management of human resources (HR) is important to achieve company goals. One of the activities in HR management is recruitment, selection, and training. Recruitment and selection are usually done not using a system so that the calculations are still done manually. But by processing data using the system can produce a decision in recommending prospective employees that can have a positive impact on the company. The company selection process is carried out through two stages: administrative selection and final selection in the form of psychological test assessment, interviews, ability tests and communication. The use of the Elimination Et Choix Traduisant La Realite (ELECTRE) method in the administrative selection stage and the Weighted Product (WP) method in the final selection stage is a new discovery made to get the best decision in accordance with the required criteria. By using this method the final results will be obtained namely the recommendation of several prospective employees who are fit to work in the company. The performance results of this system reach one hundred percent, the data from the system is in accordance with the expected calculation.
New Student Admission Selection System at PTKIN: Effectiveness of Entrance Pathways and its Relationship with Grade Point Average Mohamad Erihadiana; Undang Syaripudin
Ta'dib Vol 29 No 2 (2024): Ta'dib: Jurnal Pendidikan Islam
Publisher : Faculty of Tarbiyah and Teaching Sciences, Universitas Islam Negeri Raden Fatah Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19109/td.v29i2.25226

Abstract

This study examines the selection system for new student admissions at Islamic Religious Universities in Indonesia and its correlation with students' Grade Point Average (GPA) in UIN Sunan Gunung Djati Bandung, UIN Sunan Ampel Surabaya, UIN Walisongo Semarang. This research adopts a mixed-methods approach, integrating both quantitative and qualitative analyses. The quantitative analysis utilizes correlation techniques and descriptive statistics to examine relationships within the data. Meanwhile, the qualitative analysis employs content analysis of policies and documents, complemented by in-depth interviews with election officials and a comprehensive literature review, to investigate the transparency and efficiency of the selection system. Findings reveal no significant correlation between the specific admission pathway and the student's GPA. This suggests that the existing selection system is equally effective across different entry channels in predicting academic success as measured by GPA. The study underscores the importance of maintaining transparent, inclusive, and accountable admission processes, enhancing public trust in Islamic Religious Universities. The research contributes insights into optimizing student selection processes to align with educational goals and uphold academic standards. 
Implementation of Convolutional Neural Network CNN Algorithm to Detect Coffe Fruit Maturity Yana Aditia Gerhana; Rafi Rai Heryanto; Undang Syaripudin; Deden Suparman
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.
Analisis Sentimen Hasil Transkripsi Audio Berbahasa Indonesia Menggunakan T5 (Text-to-Text Transfer Transformer) Suhendar, Hilman; Slamet, Cepy; Syaripudin, Undang
SMATIKA JURNAL : STIKI Informatika Jurnal Vol 15 No 01 (2025): SMATIKA Jurnal : STIKI Informatika Jurnal
Publisher : LPPM STIKI MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/smatika.v15i01.1521

Abstract

In the digital era, sentiment analysis has become a vital tool for understanding public opinion, particularly from data derived from digital media such as videos. However, voice-based sentiment analysis in the Indonesian language remains uncommon. This research aims to develop the T5 model for sentiment analysis of Indonesian generated from speech using speech-to-text technology. The primary advantages of the T5 model lie in its ability to process lengthy texts, comprehend natural language context, and adapt training for specific tasks such as sentiment analysis. The research dataset was obtained from 20 YouTube videos, segmented into clips of a maximum duration of 15 seconds, resulting in a total of 300 sentences consisting of 150 positive sentiments and 150 negative sentiments. The generated text data was processed using the T5 model, which was specifically trained to detect positive and negative sentiments through the optimization of specific hyperparameters. The results demonstrated that the T5 model achieved an accuracy of 83%, with a precision of 0.85, a recall of 0.83, and an F-measure of 0.83 when tested on datasets different from the training data. This research indicates that the T5 model can be adapted for voice-based sentiment analysis in the Indonesian language with satisfactory results. These findings contribute to the development of voice-based sentiment analysis technology, which can be applied to opinion analysis or product reviews. In the future, improving the pre-processing stage and using more diverse datasets are expected to improve the overall performance of the model.
Implementasi Model CNN ResNet50V2 untuk Klasifikasi Pneumonia pada Citra X-Ray Anwar, Muhammad Afian; Gerhana, Yana Aditia; Syaripudin, Undang
SMATIKA JURNAL : STIKI Informatika Jurnal Vol 15 No 01 (2025): SMATIKA Jurnal : STIKI Informatika Jurnal
Publisher : LPPM STIKI MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/smatika.v15i01.1538

Abstract

The utilization of technology to build models that can classify pneumonia medical images automatically is needed for early diagnosis. This study aims to implement a Convolutional Neural Network (CNN) model with ResNet50V2 architecture that has been proven to have high accuracy in medical image classification. The model adopts a deep and efficient residual architecture, which facilitates deeper training of the model without suffering from vanishing gradient problem. This study went through four main stages: pneumonia and normal X-ray image data collection, data pre-processing (including set division, transformation, and augmentation), modeling using CNN with hyperparameter tuning, and model evaluation. Evaluation was performed using accuracy, F1-score, and Confusion Matrix metrics. The CNN model with ResNet50V2 as the backbone achieved 97% accuracy, showing excellent performance in differentiating between pneumonia and normal despite a small amount of misclassification. Although this model showed impressive results, challenges such as potential misclassification in cases with unclear or ambiguous images remain. Compared to previous approaches, this model offers advantages in accuracy and processing efficiency thanks to the use of a deeper and more sophisticated ResNet50V2. These advantages are expected to improve the precision of automated diagnosis in future medical applications.
Co-Authors Anwar, Muhammad Afian Arief Fatchul Huda Cecep Nurul Alam Cepy Slamet Dede K Deden Suparman, Deden Devi Novitasari Erwan Yani Ijang Badruzaman M. Ramdhani Mohamad Erihadiana, Mohamad Mohamad Irfan Muhammad Hamdani Nur Lukman Rafi Rai Heryanto Suhendar, Hilman Yana Aditia Gerhana Yana Aditia Gerhana, Yana Aditia Yogi Saputra