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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 Advanced Science and Computer Applications Adopsi Teknologi dan Sistem Informasi 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 Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Public Health Social Sciences

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Implementasi Sistem Informasi Manajemen Inventaris (SIM-VENTAR) Laboratorium Yudi Sukmono; Mualin Renaldy Setiabudi; Suwardi Gunawan; Haviluddin Haviluddin
Jurnal Rekayasa Teknologi Informasi (JURTI) Vol 6, No 1 (2022): Jurnal Rekayasa Teknologi Informasi (JURTI)
Publisher : Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/jurti.v6i1.8187

Abstract

Pengelolaan sistem inventaris pada Laboratorium Komputasi dan Pemrograman Komputer (KPK), Fakultas Teknik, Universitas Mulawarman memiliki beberapa masalah antara lain banyaknya file Microsoft Excel yang menyulitkan pencarian, kodefikasi barang yang masih belum sesuai dengan Peraturan Menteri Keuangan (PERMENKEU) Nomor 29/PMK.06/2010. Berdasarkan dari masalah yang ada pengembangan Sistem Informasi Manajemen Inventaris (SIM-VENTAR) dengan menggunakan data flow diagram (DFD) dan relasi database telah dirancang dan dibangun. Setelah itu, dilakukan uji blackbox untuk mengetahui sistem sudah berjalan sesuai dengan fungsinya. Hasil dari penelitian ini adalah terbangunnya sebuah SIM-VENTAR yang terdiri dari halaman menu utama, data inventaris, data aset, data SIMAK-BMN, data perawatan, list data inventaris, list nilai aset, list data SIMAK-BMN, list data perawatan, dan list laporan. Tahap akhir yaitu tahap dimana dilakukan pengisian data inventaris keseluruhan pada database dimana sistem ini dapat membantu pihak Laboratorium secara baik, cukup efektif dan efisien.
Implementasi Contents Management System (CMS) Dalam Analisa dan Perancangan Sistem Informasi Kepegawaian Universitas Mulawarman Muhammad Rivani Ibrahim; Bambang Nur Basuki; Agus Soepriyadi; Haviluddin Haviluddin; Dedy Mirwansyah; Anton Prafanto
Jurnal Rekayasa Teknologi Informasi (JURTI) Vol 6, No 1 (2022): Jurnal Rekayasa Teknologi Informasi (JURTI)
Publisher : Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/jurti.v6i1.7659

Abstract

Sistem Informasi berbasis website salah satu teknologi yang terdiri dari teks, gambar, file, dan suara animasi dan dengan kemampuan itu media informasi akan lebih menarik dan dimininati untuk dipergunakan sebagai media penyebaran informasi. Sistem Informasi berbasis website mampu mengolah data menjadi sebuah informasi dengan cara mengidentifikasi, mengumpulkan, mengelola dan menyediakan untuk diakses secara bersama-sama. Sistem informasi website Koordinator Kepegawaian Universitas Mulawarman merupakan sistem yang dipergunakan untuk memberikan kemudahan dalam mendapatkan informasi yang dibutuhkan pengguna dan selain itu, informasi yang di manajemenn oleh website akan tersimpan dengan baik dan tidak mudah hilang pada sistem sehingga memudahkan pegawai dalam mencari informasi. Sistem Informasi Koordinator Kepegawaian menggunakan sistem Content Management System (CMS) dalam implementasinya karena kemampuan dari CMS yang cocok dengan permasalahan sistem yang ada pada Koordinator Kepegawaian dan penelitian ini menggunakan metode waterfall dalam perencangan sistem Koordinator Kepegawaian. Hasil pada penilitan ini yaitu, informasi lebih mudah dicari dan tidak mudah hilang karena sistem CMS ini mampu membuat kategori-kategori berdasarkan kebutuhan Koordinator Kepegawaian.
Drugs Consumption Clustering Model using K-Means Technique Haviluddin Haviluddin; Yudi Sukmono; Didit Suprihanto; Arif Harjanto; Olivia Angelica Murtioso
CESS (Journal of Computer Engineering, System and Science) Vol 7, No 2 (2022): July 2022
Publisher : Universitas Negeri Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24114/cess.v7i2.36104

Abstract

Persediaan obat pada suatu Puskesmas seringkali habis sebelum jadwal penerimaan obat dilakukan hal ini dikarenakan Kejadian Luar Biasa (KLB). Sehingga, perencanaan persediaan obat yang efektif dan efisien dengan menerapkan metode kecerdasan buatan dalam rangka membantu pihak manajemen sangat diperlukan. Penelitian ini bertujuan untuk memonitoring persediaan obat sebagai salah satu dasar dalam permintaan obat. Data pemakaian obat yang digunakan berasal dari Laporan Pemakaian dan Lembar Permintaan Obat (LPLPO) UPTD Puskesmas Lempake tahun 2016-2018 dan telah dinormalisasi dengan metode Z-Score. Metode K-Means telah diterapkan sebanyak 3 cluster terdiri tinggi (C1), sedang (C2) dan kurang (C3) dimana penentuan titik centroid berdasarkan nilai max, average, dan min. Sedangkan, metode jarak Euclidean distance telah ditetapkan untuk menganalisis jarak data tiap cluster. Hasil temuan mengindikasikan bahwa pengujian cluster menggunakan Sum of Squared Error (SSE) telah mendapatkan nilai sebesar 77,34814. Dimana, hasil pengelompokkan yaitu C1 sebanyak 5 data, C2 sebanyak 14 data, dan C3 sebanyak 206 data. Hal ini berarti bahwa 3 cluster merupakan hasil terbaik pengelompokkan. Metode K-Means dapat menjadi alternatif dalam membuat model analisis monitoring persediaan obat di Puskemas.
Peramalan Pelayanan Service Mobil (After-Sale) Menggunakan Backpropagation Neural Network (BPNN) Novianti Puspitasari; Haviluddin; Arinda Mulawardani Kustiawan; Hario Jati Setyadi; Gubtha Mahendra Putra
JISKA (Jurnal Informatika Sunan Kalijaga) Vol. 6 No. 3 (2021): September 2021
Publisher : UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (479.913 KB) | DOI: 10.14421/jiska.2021.6.3.149-160

Abstract

The automotive industry in Indonesia, primarily cars, is getting more and more varied. Along with increasing the number of vehicles, Brand Holder Sole Agents (ATPM) compete to provide after-sale services (mobile service). However, the company has difficulty knowing the rate of growth in the number of mobile services handled, thus causing losses that impact sources of income. Therefore, we need a standard method in determining the forecasting of the number of car services in the following year. This study implements the Backpropagation Neural Network (BPNN) method in forecasting car service services (after-sale) and Mean Square Error (MSE) for the process of testing the accuracy of the forecasting results formed. The data used in this study is car service data (after-sale) for the last five years. The results show that the best architecture for forecasting after-sales services using BPNN is the 5-10-5-1 architectural model with a learning rate of 0.2 and the learning function of trainlm and MSE of 0.00045581. This proves that the BPNN method can predict mobile service (after-sale) services with good forecasting accuracy values.
Identifikasi Senjata Tradisional Mandau Suku Dayak Menggunakan Metode Support Vector Machine Masna Wati; Delvina Dwiani Samjar; Haviluddin Haviluddin; Faza Alameka
METIK JURNAL Vol 6 No 1 (2022): METIK Jurnal
Publisher : LPPM Universitas Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47002/metik.v6i1.341

Abstract

Mandau is a sharp weapon like a machete that comes from the culture of the Dayak tribe in Kalimantan. Mandau itself has many types of carvings with different motifs in each typical Dayak tribe. The diversity of Dayak clumps and sub-tribes produces various types of Mandau, which are similar. Most Indonesians cannot distinguish between the existing variety of Mandau because of the information lack about Mandau. The study aims to identify Mandau types by applying Support Vector Machine (SVM) methods according to shape and texture feature extraction of image Mandau. Process of feature extraction using eccentricity, metric, contrast, correlation, energy, and homogeneity parameters. Six parameters obtained from the image feature extraction were used for the classification process. There are four kind of Mandau that used, such as Mandau Benuaq, Mandau Kenyah, Mandau Mahah, and Mandau Tunjung, with each Mandau 24 images, and the total data is 96. The implementation of the SVM method in identifying the type of Mandau using Linear Kernel, Gaussian / RBF Kernel, and Polynomial Kernel. The results indicated that when SVMs were used to identify Mandau images in split data, 80% of the training and 20% of the test data had the highest average accuracy of 82%. Based on the type of Kernel at 80:20 data split, the identification of the Mandau image using the Polynomial Kernel has the highest accuracy rate of 95%.
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).
Adaptive Neuro-Fuzzy Inference System for Waste Prediction Haviluddin Haviluddin; Herman Santoso Pakpahan; Novianti Puspitasari; Gubtha Mahendra Putra; Rima Yustika Hasnida; Rayner Alfred
Knowledge Engineering and Data Science Vol 5, No 2 (2022)
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um018v5i22022p122-128

Abstract

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.
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.
Co-Authors Achmad Fanany Onnilita Gaffar Achmad Fanany Onnilita Gaffar Adnan, Adam Agus Soepriyadi Ahmad Hijazi, Mohd Hanafi Ahmad Jawahir Ahmad Jawahir Aiman, Ahmad Zuhair Nur Aina Musdholifah Aji Prasetya Wibawa Akhmad Masyudi 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 Cholisah Erman Hasihi 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 Fahrul Agus 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, Emmilya Umma Aziza Gubtha Mahendra Putra Gubtha Mahendra Putra Gultom, Tiopan Hendry Manto Hairah, Ummul Hamdani Hamdani Hatta, Heliza Rahmania Heliza Rahmania Hatta, Heliza Rahmania Helmi Puadi, Fazma Urmila Jannah Herdianti Darwis Herlina Jayadiyanti 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 Iwan Muhamad Ramdan Izdihar, Zahra Nabila Jainuddin Jainuddin Jayadiyanti, Herlina Julius Rinaldi Simanungkalit Kesuma, Muhammad Afrizal Kim On, Chin Leong, Jing Mei Lilik Hendrajaya Malani, Rheo Maratus Soleha Masyudi, Akhmad Mega Yoalifa Ming Foey Teng 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 Nurfaizi Amin Olivia Angelica Murtioso Omar Mohammed Barukab Omar Obarukab Norazah Yusof Othman, Mohd Shahizan Pakpahan, Herman Santoso Paroliyan, Abraham Pradinata, Muhammad Aji Prafanto, Anton Pratama, Arief Ardi Prawira, Muhammad Nanda Purnawansyah Purnawansyah Puspitasari, Novianti Putut Pamilih Widagdo, Putut Pamilih Qonita, Adiba Rahayu, Ervina Raja, Roesman Ridwan Rayner Alfred Rayner Alfred Rayner Alfred Rayner Alfred Rayner Alfred Rayner Alfred Rendy Ramadhan Rima Yustika Hasnida 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 Triyanna Widiyaningtyas Triyanna Widyaningtyas 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 Yudi Sukmono, Yudi Yulita Salim Yunianta, Arda Yusof, Omar Obarukab Norazah Zainal Arifin Zainal Arifin