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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
Haviluddin Haviluddin
(SCOPUS ID: 56596793000, Universitas Mulawarman)
Aerospace Engineering Agriculture, Biological Sciences & Forestry Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Public Health Social Sciences Transportation

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Analisa Kebutuhan Tenaga Kesehatan Menggunakan Algoritma K-Means Brins Leonard Pailan; Haviluddin Haviluddin; Masna Wati; Novianti Puspitasari; Edy Budiman
Sains, Aplikasi, Komputasi dan Teknologi Informasi Vol 3, No 1 (2021): Sains, Aplikasi, Komputasi dan Teknologi Informasi
Publisher : Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/jsakti.v3i1.4406

Abstract

Penempatan tenaga kesehatan yang bersesuaian keahlian pada Pusat Kesehatan Masyarakat (Puskesmas) sangat diperlukan namun belum merata terdistribusi. Penelitian ini bertujuan untuk menganalisa tenaga kesehatan yang sesuai bidang keahliannya untuk ditempatkan pada setiap Puskesmas di Provinsi Kalimantan Timur. Tenaga kesehatan yang bersesuaian keahlian dianalisa untuk ditempatkan pada 179 Puskesmas di kawasan perkotaan, kawasan pedesaan, dan kawasan terpencil/sangat terpencil dengan menggunakan algoritma cerdas yaitu algoritma K-Means. Berdasarkan percobaan, Puskesmas terbagai ke dalam 4 kelompok terdiri dari kelompok 1 sebanyak 82 Puskesmas kawasan pedesaan dengan fasilitas non rawat inap; kelompok 2 sebanyak 23 Puskesmas kawasan perkotaan dengan fasilitas non rawat inap; kelompok 3 sebanyak 59 Puskesmas kawasan pedesaan dengan fasilitas rawat inap; dan kelompok 4 sebanyak 15 Puskesmas kawasan perkotaan dengan fasilitas rawat inap. Hasil penelitian memperlihatkan bahwa Puskesmas kelompok 1 masih kekurangan tenaga kesehatan dokter gigi dan ahli gizi; Puskesmas kelompok 2 masih kekurangan tenaga kesehatan farmasi; Puskesmas kelompok 3 masih kekurangan tenaga kesehatan ahli gizi; dan Puskesmas kelompok 4 sudah memenuhi standar minimal tenaga kesehatan. Hal ini menunjukkan bahwa pengelompokkan Puskesmas tersebut dapat dijadikan acuan dalam mengambil kebijakan yang diharapkan dapat menjawab permasalahan distribusi penempatan tenaga kesehatan yang tidak merata di Provinsi Kalimantan Timur.
Analisa Mutu Sekolah Pada Provinsi Kalimantan Timur Menggunakan Algoritma K-Means Mega Yoalifa; Haviluddin Haviluddin; Masna Wati; Novianti Puspitasari; Ummul Hairah
Sains, Aplikasi, Komputasi dan Teknologi Informasi Vol 3, No 2 (2021): Sains, Aplikasi, Komputasi dan Teknologi Informasi
Publisher : Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/jsakti.v3i2.4407

Abstract

Penelitian ini bertujuan untuk mengelompokkan Sekolah Menengah Atas (SMA) berdasarkan Standar Mutu Pendidikan (SNP) sehingga memiliki kategori Standar Tinggi (C1), Standar Sedang (C2), dan Standar Rendah (C3) di Daerah Kutai Barat dan Kutai Kartanegara, Provinsi Kalimantan Timur. Metode analisa telah menggunakan algoritma K-Means dengan tiga metode perhitungan jarak yaitu Euclidean Distance, Manhattan Distance, dan Minkowski Distance. Berdasarkan hasil percobaan dengan Euclidean Distance dan Minkowski Distance terdapat 9 sekolah dengan perhitungan akurasi sum of square error (SSE) sebesar 42.6793 dalam kategori berstandar tinggi (C1), 48 sekolah dengan akurasi perhitungan SSE sebesar 26.6885 berkategori standar sedang (C2), dan 3 sekolah dengan akurasi perhitungan SSE sebesar 52.6727 berkategori standar rendah (C3). Hasil penelitian ini diharapkan menjadi rekomendasi dalam memberikan program kerja peningkatan mutu dan kualitas SMA oleh pihak-pihak terkait seperti Dinas Pendidikan dan Kebudayaan (Dikbud).
Multi-step CNN forecasting for COVID-19 multivariate time-series Haviluddin Haviluddin; Rayner Alfred
International Journal of Advances in Intelligent Informatics Vol 9, No 2 (2023): July 2023
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v9i2.1080

Abstract

The new coronavirus (COVID-19) has spread to over 200 countries, with over 36 million confirmed cases as of October 10, 2020. As a result, numerous machine learning models capable of forecasting the epidemic worldwide have been produced. This paper reviews and summarizes the most relevant machine learning forecasting models for COVID-19. The dataset is derived from the world health organization (WHO) COVID-19 dashboard, and it contains official daily counts of COVID-19 cases, fatalities, and vaccination use reported by countries, territories, and regions. We propose various convolutional neural network (CNN) based models such as CNN, single exponential smoothing CNN (S-CNN), moving average CNN (MA-CNN), smoothed moving average CNN (SMA-CNN), and moving average smoothed CNN (MAS-CNN). Here, MAPE and MSE are used to assess the suggested models. MAPE is frequently used to compare accuracy across time series with different scales. MSE, the model must strive for a total forecast equal to the entire demand. That is, optimizing MSE seeks to create a forecast that is right on average and so unbiased. The final result shows that SMA-CNN outperformed its baselines in both MAPE and MSE. The main contribution of this novel forecasting approach is a more accurate result as a base of the strategy of preventing COVID-19 spreads.
Autoregressive Integrated Moving Average (ARIMA) Model for Forecasting Indonesian Crude Oil Price Masna Wati; Haviluddin Haviluddin; Akhmad Masyudi; Anindita Septiarini; Heliza Rahmania Hatta
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.v9i3.22286

Abstract

Crude oil is the main commodity of the global economy because oil is used as an ingredient for many industries globally and is the price base used in the state budget. Indonesian Crude Price (ICP) fluctuates following developments in world crude oil prices. A significant increase in crude oil prices will certainly disrupt the economy. Thus, the movement or fluctuation of ICP is essential for business players in the energy market, especially domestically. Therefore, crude oil price forecasting is needed to assist business people in making decisions related to the energy market. This study aims to find a suitable forecasting model for Indonesian crude oil prices using the Autoregressive Integrated Moving Average (ARIMA) method. The forecasting process used ICP time-series data per month for 50 types of crude oil within five years or 63 months. Based on the experimental results, it was found that the most fit ARIMA models were (0,1,1), (1,1,0), (0,1,0), and (1,2,1). The test results for April to September 2020 have a good and proper interpretation, except the type of BRC oil indicates inaccurate forecasts. The ARIMA error rate is very dependent on the value of the data before it is predicted and external factors, the more unstable the data value every month, the higher the error rate.
Big data: issues trends problems controversies in ASEAN perspective Haviluddin, Haviluddin; Alfred, Rayner
Bulletin of Social Informatics Theory and Application Vol. 3 No. 2 (2019)
Publisher : Association for Scientific Computing Electrical and Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/businta.v3i2.239

Abstract

Big Data has a characteristics is size, new opportunities and have the potential to transform corporations and government and its interactions with the public. This paper attempts to offer a broader definition of Big Data that captures it is other unique and defining characteristics. This paper presents a consolidated description of Big Data by integrating definitions from practitioners and academics. In addition, we summarize the issues, trends, problems and controversies related to Big Data (technology, applications, and people) from infrastructure (i.e., hardware and software), technology for Big Data Analytics (BDA), management, educational and scientists, and government-related to policies perspectives in order to support the Economic Community ASEAN (AEC) era.
DIET Classifier Model Analysis for Words Prediction in Academic Chatbot Astuti, Wistiani; Wibawa, Aji Prasetya; Haviluddin, Haviluddin; Darwis, Herdianti
ILKOM Jurnal Ilmiah Vol 16, No 1 (2024)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v16i1.1598.59-67

Abstract

One prevalent conversational system within the realm of natural language processing (NLP) is chatbots, designed to facilitate interactions between humans and machines. This study focuses on predicting frequently asked questions by students using the Duel Intent and Entity Transformer (DIET) Classifier method and assessing the performance of this method. The research involves employing 300 epochs with an 80% training data and 20% testing data split. In this study, the DIET Classifier adopts a multi-task transformer architecture to simultaneously handle classification and entity recognition tasks. Notably, it possesses the capability to integrate diverse word embeddings, such as BERT and GloVe, or pre-trained words from language models, and blend them with sparse words and n-gram character-level features in a plug-and-play manner. Throughout the training process of the DIET Classifier model, data loss and accuracy from both training and testing datasets are monitored at each epoch. The evaluation of the text classification model utilizes a confusion matrix. The accuracy results for testing the DIET Classifier method are presented through four case studies, each comprising 25 text messages and 15 corresponding chatbot responses. The obtained accuracy values range from 0.488 to 0.551, F1-Score values range from 0.427 to 0.463, and precision range from 0.417 to 0.457.
Implementasi Metode User Experience Questionnaire Pada Website Kepegawaian Universitas Mulawarman Ibrahim, Muhammad Rivani; Soepriyadi, Agus; Basuki, Nur Bambang; Sutikno, Sutikno; Haviluddin, Haviluddin; Widagdo, Putut Pamilih
Jurnal Rekayasa Teknologi Informasi (JURTI) Vol 8, No 1 (2024): Jurnal Rekayasa Teknologi Informasi (JURTI)
Publisher : Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/jurti.v8i1.15772

Abstract

Website Kepegawaian Universitas Mulawarman (Unmul) merupakan salah satu sarana penting bagi para pegawai Unmul untuk mengakses informasi dan layanan kepegawaian. Untuk mengetahui efektivitas dan efisiensi website dalam memenuhi kebutuhan penggunanya, dilakukan evaluasi website menggunakan metode User Experience Questionnaire (UEQ). Penelitian ini melibatkan 50 pegawai Unmul yang dipilih secara acak. Data dikumpulkan melalui kuesioner UEQ yang terdiri dari 6 dimensi, yaitu: Kegunaan untuk Mengukur kemudahan penggunaan website, Keefektifan untuk Mengukur kemampuan website dalam membantu pengguna mencapai tujuan. Kepuasan untuk Mengukur tingkat kepuasan pengguna terhadap website. Kemampuan belajar untuk Mengukur kemudahan pengguna dalam mempelajari cara menggunakan website. Memorability untuk Mengukur kemampuan pengguna dalam mengingat cara menggunakan website. Kesalahan untuk Mengukur tingkat kesalahan yang dilakukan pengguna saat menggunakan website. Hasil penelitian menunjukkan bahwa website Kepegawaian Unmul memiliki skor UEQ yang cukup baik secara keseluruhan, dengan nilai tertinggi pada dimensi kegunaan dan nilai terendah pada dimensi kemampuan belajar. Hal ini menunjukkan bahwa website tersebut mudah digunakan dan membantu pengguna dalam mencapai tujuan, namun masih perlu ditingkatkan dalam hal kemudahan mempelajari cara penggunaannya. Berdasarkan hasil evaluasi, beberapa rekomendasi untuk meningkatkan website Kepegawaian Unmul diajukan, antara lain: Menyediakan panduan pengguna yang lebih lengkap dan mudah dipahami, Meningkatkan desain website agar lebih intuitif dan menarik, Melakukan pengujian usability secara berkala untuk mengidentifikasi dan memperbaiki masalah yang ada. Dengan menerapkan rekomendasi tersebut, diharapkan website Kepegawaian Unmul dapat menjadi lebih efektif dan efisien dalam memenuhi kebutuhan para penggunanya.
Automated water quality monitoring and regression-based forecasting system for aquaculture Wei, Toh Yin; Tindik, Emmanuel Steward; Fui, Ching Fui; Haviluddin, Haviluddin; Hijazi, Mohd Hanafi Ahmad
Bulletin of Electrical Engineering and Informatics Vol 12, No 1: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i1.4464

Abstract

Water quality in fish tanks is essential to reduce fish mortality. Many factors affect the water quality, such as pH, dissolved oxygen, and temperature in fish tanks. Existing work has presented water quality monitoring systems for aquaculture, which are useful for automatic monitoring and notify any incidence of decline in water quality. It enables the fish farms to make interventions to reduce fish mortality. However, advanced monitoring through forecasting is necessary to ensure consistent optimum water quality. This paper presents a web-based water quality monitoring and forecasting system for aquaculture. First, a water quality forecasting model based on the long short-term memory is designed and developed. The model is evaluated and fine-tuned using the existing public dataset. Second, the prototype of the water quality monitoring and forecasting system is developed. An Arduino and Raspberry Pi based water quality data acquisition tool is built. A web-based application is then developed to present the monitoring data and forecasting. A notification module is included to send an alert message to the fish farmers when necessary. The system is tested and evaluated at the fish hatchery in Universiti Malaysia Sabah. The findings show that the proposed system provides better water quality management for fish farms.
The development and usability test of an automated fish counting system based on CNN and contrast limited histogram equalization Leong, Jing Mei; Ahmad Hijazi, Mohd Hanafi; Saudi, Azali; Kim On, Chin; Fui Fui, Ching; Haviluddin, Haviluddin
Bulletin of Electrical Engineering and Informatics Vol 13, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i2.5840

Abstract

The aquaculture industry has rapidly grown over the year. One pertinent aspect is the ability of the aquaculture farm management to accurately count the fish populations to provide effective feeding and the control of breeding density. The current practice of counting the fish manually increased the hatchery workers workload and led to inefficiency. The presented work proposed an intelligent, web-based fish counting system to assist hatchery workers in counting fish from images. The methodology consists of two phases. First, an intelligent fish counting engine is developed. The captured image was first enhanced using the contrast limited adaptive histogram equalization. A deep learning architecture in the form of you only look once (YOLO)v5 is used to generate a model to identify and count fish on the image. Second, a web-based application is developed to implement the developed fish counting engine. When applied to the test data, the developed engine recorded a precision of 98.7% and a recall of 65.5%. The system is also evaluated by hatchery workers in the University Malaysia Sabah fish hatchery. The results of the usability and functionality evaluations indicate that the system is acceptable, with some future work suggested based on the feedback received.
Early Stopping on CNN-LSTM Development to Improve Classification Performance Anam, M. Khairul; Defit, Sarjon; Haviluddin, Haviluddin; Efrizoni, Lusiana; Firdaus, Muhammad Bambang
Journal of Applied Data Sciences Vol 5, No 3: SEPTEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i3.312

Abstract

Currently, CNN-LSTM has been widely developed through changes in its architecture and other modifications to improve the performance of this hybrid model. However, some studies pay less attention to overfitting, even though overfitting must be prevented as it can provide good accuracy initially but leads to classification errors when new data is added. Therefore, extra prevention measures are necessary to avoid overfitting. This research uses dropout with early stopping to prevent overfitting. The dataset used for testing is sourced from Twitter; this research also develops architectures using activation functions within each architecture. The developed architecture consists of CNN, MaxPooling1D, Dropout, LSTM, Dense, Dropout, Dense, and SoftMax as the output. Architecture A uses default activations such as ReLU for CNN and Tanh for LSTM. In Architecture B, all activations are replaced by Tanh, and in Architecture C, they are entirely replaced by ReLU. This research also performed hyperparameter tuning such as the number of layers, batch size, and learning rate. This study found that dropout and early stopping can increase accuracy to 85% and prevent overfitting. The best architecture entirely uses ReLU activation as it demonstrates advantages in computational efficiency, convergence speed, the ability to capture relevant patterns, and resistance to noise.
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 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 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 Medi Taruk 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 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