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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Informatics and Communication Technology (IJ-ICT) Bulletin of Electrical Engineering and Informatics Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) International Journal of Advances in Intelligent Informatics CESS (Journal of Computer Engineering, System and Science) Proceeding of the Electrical Engineering Computer Science and Informatics Sistemasi: Jurnal Sistem Informasi Jurnal Teknologi dan Sistem Komputer Informatika Mulawarman: Jurnal Ilmiah Ilmu Komputer Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) International Journal of Artificial Intelligence Research Knowledge Engineering and Data Science JIKO (Jurnal Informatika dan Komputer) International Journal of Computing and Informatics (IJCANDI) JURNAL REKAYASA TEKNOLOGI INFORMASI ILKOM Jurnal Ilmiah Prosiding SAKTI (Seminar Ilmu Komputer dan Teknologi Informasi) METIK JURNAL JISKa (Jurnal Informatika Sunan Kalijaga) Sains, Aplikasi, Komputasi dan Teknologi Informasi Indonesian Journal of Electrical Engineering and Computer Science JUKI : Jurnal Komputer dan Informatika Jurnal Teknik Informatika (JUTIF) Journal of Applied Data Sciences International Journal of Engineering, Science and Information Technology Insyst : Journal of Intelligent System and Computation International Journal of Advanced Science and Computer Applications Adopsi Teknologi dan Sistem Informasi Information Technology Education Journal Bulletin of Social Informatics Theory and Application Periodicals of Occupational Safety and Health Pengabdian Kepada Masyarakat Bidang Teknologi dan Sistem Informasi The Indonesian Journal of Computer Science
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Journal : knowledge engineering and data science

Crude Palm Oil Prediction Based on Back propagation Neutral Network Approach Aini, Hijratul; Haviluddin, Haviluddin
Knowledge Engineering and Data Science
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Crude palm oil (CPO) production at PT. Perkebunan Nusantara (PTPN) XIII from January 2015 to January 2018 have been treated. This paper aims to predict CPO production using intelligent algorithms called Backpropagation Neural Network (BPNN). The accuracy of prediction algorithms have been measured by mean square error (MSE). The experiment showed that the best hidden layer architecture (HLA) is 5-10-11-12-13-1 with learning function (LF) of trainlm, activation function (AF) of logsig and purelin, and learning rate (LR) of 0.5. This architecture has a good accuracy with MSE of 0.0643. The results showed that this model can predict CPO production in 2019.
Handwriting Character Recognition usingVector Quantization Technique Haviluddin, Haviluddin; Alfred, Rayner; Moham, Ni’mah; Pakpahan, Herman Santoso; Islamiyah, Islamiyah; Setyadi, Hario Jati
Knowledge Engineering and Data Science
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This paper seeks to explore Learning Vector Quantization (LVQ) processing stage to recognize The Buginese Lontara script from Makassar as well as explaining its accuracy. The testing results of LVQ obtained an accuracy degree of 66.66 %. The most optimal variant of network architecture in the recognition process is a variation of learning rate of 0.02, a maximum epoch of 5000 and a hidden layer of 90 neurons which was the result of recognition based on feature 8. Based on these variations, the obtained performance with a mean square error (MSE) of 0.0306 and the time required during the learning process was quite short, 6 minutes and 38 seconds. Based on the results of the testing, the LVQ method has not been able to provide good recognition results and still requires development to generate better recognition results.
Backpropagation Neural Network with Combination of Activation Functions for Inbound Traffic Prediction Purnawansyah, Purnawansyah; Haviluddin, Haviluddin; Darwis, Herdianti; Azis, Huzain; Salim, Yulita
Knowledge Engineering and Data Science
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Predicting network traffic is crucial for preventing congestion and gaining superior quality of network services. This research aims to use backpropagation to predict the inbound level to understand and determine internet usage. The architecture consists of one input layer, two hidden layers, and one output layer. The study compares three activation functions: sigmoid, rectified linear unit (ReLU), and hyperbolic Tangent (tanh). Three learning rates: 0.1, 0.5, and 0.9 represent low, moderate, and high rates, respectively. Based on the result, in terms of a single form of activation function, although sigmoid provides the least RMSE and MSE values, the ReLu function is more superior in learning the high traffic pattern with a learning rate of 0.9. In addition, Re-LU is more powerful to be used in the first order in terms of combination. Hence, combining a high learning rate and pure ReLU, ReLu-sigmoid, or ReLu-Tanh is more suitable and recommended to predict upper traffic utilization.
Adaptive Neuro-Fuzzy Inference System for Waste Prediction Haviluddin, Haviluddin; Pakpahan, Herman Santoso; Puspitasari, Novianti; Putra, Gubtha Mahendra; Hasnida, Rima Yustika; Alfred, Rayner
Knowledge Engineering and Data Science
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The volume of landfills that are increasingly piled up and not handled properly will have a negative impact, such as a decrease in public health. Therefore, predicting the volume of landfills with a high degree of accuracy is needed as a reference for government agencies and the community in making future policies. This study aims to analyze the accuracy of the Adaptive Neuro-Fuzzy Inference System (ANFIS) method. The prediction results' accuracy level is measured by the value of the Mean Absolute Percentage Error (MAPE). The final results of this study were obtained from the best MAPE test results. The best predictive results for the ANFIS method were obtained by MAPE of 3.36% with a data ratio of 6:1 in the North Samarinda District. The study results show that the ANFIS algorithm can be used as an alternative forecasting method.
Comparative Analysis of BPNN and LVQ for Sundanese Character Recognition Haviluddin, Haviluddin; Pakpahan, Herman Santoso; Nurpadillah, Dinda Izmya; Setyadi, Hario Jati; Taruk, Medi; Alfred, Rayner
Knowledge Engineering and Data Science
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Abstract

The Sundanese script (Aksara Sunda), an essential part of Sundanese cultural heritage, has been used since the 14th century AD. However, recognizing handwritten Sundanese characters remains challenging due to variations in individual writing styles. This study compares the performance of Backpropagation Neural Network (BPNN) and Learning Vector Quantization (LVQ) for recognizing handwritten Sundanese vowel (Swara) characters. A dataset was collected from 15 individuals, each writing seven Sundanese vowel characters, which were then used for training and testing the recognition models. Experimental results show that BPNN outperforms LVQ, achieving a higher classification accuracy (95.23%), lower Mean Squared Error (MSE), and faster convergence compared to LVQ, which reached a maximum accuracy of 66.66%. Additionally, BPNN demonstrated better generalization and robustness. At the same time, LVQ was highly sensitive to learning rate variations, leading to unstable accuracy and slower training times. The findings highlight that BPNN is a more effective model for Sundanese script recognition, providing a reliable approach for preserving and digitizing traditional scripts. Future research should explore hybrid models, deep learning approaches, and larger datasets to enhance recognition accuracy and system robustness.
Network Traffic Time Series Performance Analysisusing Statistical Methods Purnawansyah, Purnawansyah; Haviluddin, Haviluddin; Alfred, Rayner; Gaffar, Achmad Fanany Onnlita
Knowledge Engineering and Data Science
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Abstract

This paper presents an approach for a network traffic characterization by using statistical techniques. These techniques are obtained using the decomposition, winter’s exponential smoothing and autoregressive integrated moving average (ARIMA). In this paper, decomposition and winter’s exponential smoothing techniques were used additive and multiplicative model. Then, ARIMA based-on Box-Jenkins methodology. The results of ARIMA (1,0,2) was shown the best model that can be used to the internet network traffic forecasting
Co-Authors Achmad Fanany Onnilita Gaffar Achmad Fanany Onnilita Gaffar Adnan, Adam Afdal Jamil Tanjung Agus Soepriyadi Ahmad Hijazi, Mohd Hanafi Ahmad Jawahir Ahmad Jawahir Aiman, Ahmad Zuhair Nur Aina Musdholifah Aini, Hijratul Aji Prasetya Wibawa Akhmad Masyudi Albertus Juvensius Pontus Aldi Bastiatul Fawait Fawait Alfiansyah, M Nur Ali Sholihin Allo, Adriati Manuk Anam, M Khairul Anggari, Ricky Anindita Septiarini, Anindita Anton Prafanto Arda Yunianta Arda Yunianta Arif Bramantoro Arif Harjanto Arinda Mulawardani Kustiawan Astuti, Wistiani Aulia Rahman Awang Harsa Kridalaksana Bambang Nur Basuki Bangkit Bekti Nurdianto Basuki, Nur Bambang Brins Leonard Pailan Budiman, Edy Burhandenny, Aji Ery Cahyani, Oktari Indi Cahyani, Oktaria Indi Cellia Auzia Nugraha Chrisman Bonor Sinaga Darwis, Herdianti Davina Putri Ananta Dedy Cahyadi Dedy Mirwansyah Delvina Dwiani Samjar Dhanar Intan Surya Saputra Dhanar Intan Surya Saputra Didit Suprihanto, Didit Dinda Izmya Nurpadillah Djoko Setyadi Dwiyanto, Felix Andika Efrizoni, Lusiana Emmilya Umma Azizah Gaffar Fahrul Agus Faizul Anwar Wandi Fatkhul Hani Rumawan Fauzan, Ammar Nabil Faza Alameka Fazma Urmila Jannah Helmi Puadi Firdaus, Ardhifa Firdaus, Muhammad Bambang Fui Fui, Ching Fui, Ching Fui Gaffar, Achmad Fanany Onnlita Gubtha Mahendra Putra Gubtha Mahendra Putra Gultom, Tiopan Hendry Manto Hairah, Ummul Hamdani Hamdani Hasihi, Cholisah Erman Hasnida, Rima Yustika Hatta, Heliza Rahmania Helmi Puadi, Fazma Urmila Jannah Herdianti Darwis Herlina Jayadiyanti Herman Santoso Pakpahan Hersa Safitri Hery Widijanto Hijazi, Mohd Hanafi Ahmad Hijratul Aini Hijratul Aini Huzain Azis Ibrahim, Muhammad Rivani Ifandi, Muhammad Imam Tahyudin Imam Tahyudin Irwan Gani Islamiyah Islamiyah Islamiyah Islamiyah Islamiyah Islamiyah, Islamiyah Iwan Muhamad Ramdan Izdihar, Zahra Nabila Jainuddin Jainuddin Jayadiyanti, Herlina Kesuma, Muhammad Afrizal Kim On, Chin Leong, Jing Mei Lilik Hendrajaya Malani, Rheo Maratus Soleha Mega Yoalifa Milkhatun, Milkhatun Ming Foey Teng, Ming Foey Moham, Ni’mah Mohd Shahizan Othman Mohd Shahizan Othman Mualin Renaldy Setiabudi Muhammad Bambang Muhammad Rafif Hanif Muhammad Soleh Muhammad Sultan, Muhammad Muhammad Syarif Abdillah Nafalski, Andrew Nataniel Dengen Ngurah Satria Darmawangsa Ni’mah Moham Norazah Yusof Novianti Puspitasari Nugraha, Cellia Auzia Nugroho, Basuki Rahmat Nur Fadhilah Nurfaizi Amin Nurpadillah, Dinda Izmya Olivia Angelica Murtioso Omar Mohammed Barukab Omar Obarukab Norazah Yusof Othman, Mohd Shahizan Paroliyan, Abraham Pradinata, Muhammad Aji Prafanto, Anton Pratama, Arief Ardi Prawira, Muhammad Nanda Purnawansyah Purnawansyah Puspitasari, Novianti Putra, Gubtha Mahendra Putut Pamilih Widagdo, Putut Pamilih Qonita, Adiba Rahayu, Ervina Raihanfitri Adi Kalipaksi Raja, Roesman Ridwan Rayner Alfred Rayner Alfred Rayner Alfred Rayner Alfred Rayner Alfred Rayner Alfred Rendy Ramadhan Revia Oktaviani Rima Yustika Hasnida Salim, Yulita Saputra, Irzan Tri Sarjon Defit Saudi, Azali Setyadi, Hario Jati Simanungkalit, Julius Rinaldi Sitompul, Tua Delima Soepriyadi, Agus Suryani Junita Patandianan Sutikno Sutikno Suwardi Gunawan Taruk, Medi Tindik, Emmanuel Steward Tommy Trides Triyanna Widiyaningtyas Triyanna Widyaningtyas, Triyanna Utama, Agung Bella Putra Utomo Pujianto Vina Zahrotun Kamila Wandi, Faizul Anwar Wati, Masna Wei, Toh Yin Widians, Joan Angelina Wong, Kelvin Yahya, Fiqri Khaidar Yazeed Al Moaiad Yudhi Saputra Yudi Sukmono Yulita Salim Yunianta, Arda Yusof, Omar Obarukab Norazah Zainal Arifin Zainal Arifin Zakaria Ahmad Dahlan