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All Journal Jurnal technoscientia Jurnal Informatika dan Teknik Elektro Terapan ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika International Journal of Artificial Intelligence Research Informatics for Educators and Professional : Journal of Informatics KOPERTIP: Jurnal Ilmiah Manajemen Informatika dan Komputer JURNAL ILMIAH INFORMATIKA Conference SENATIK STT Adisutjipto Yogyakarta Jurnal ICT : Information Communication & Technology Jurnal Sistem Informasi Kaputama (JSIK) Jurnal Pelayanan dan Pengabdian Masyarakat (Pamas) JISKa (Jurnal Informatika Sunan Kalijaga) Jurnal Informatika dan Rekayasa Perangkat Lunak JURSIMA (Jurnal Sistem Informasi dan Manajemen) JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Teknik Informatika C.I.T. Medicom JURNAL TEKNOLOGI TECHNOSCIENTIA Jurnal Informatika Terpadu Baselang: Jurnal Ilmu Pertanian, Peternakan, Perikanan dan Lingkungan Jurnal Janitra Informatika dan Sistem Informasi Abdimas Altruis: Jurnal Pengabdian Kepada Masyarakat Jurnal Informatika Teknologi dan Sains (Jinteks) International Journal of Hydrological and Environmental for Sustainability Literasi: Jurnal Pengabdian Masyarakat dan Inovasi J-Icon : Jurnal Komputer dan Informatika Jurnal Teknologi Pangan dan Industri Perkebunan JURSIMA AMMA : Jurnal Pengabdian Masyarakat Jurnal Informatika: Jurnal Pengembangan IT Jurnal Sistem Informasi dan Manajemen Jurnal Accounting Information System (AIMS) INTERNAL (Information System Journal)
Martanto .
STMIK IKMI Cirebon
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PENGELOMPOKAN HASIL BELAJAR SISWA PADA MASA COVID-19 DENGAN ALGORITMA K-MEANS UNTUK MENJAMIN MUTU PENDIDIKAN DI SMK BINA CENDEKIA Maulana Jamaludin; Martanto Martanto; Agus Bahtiar
JURSIMA (Jurnal Sistem Informasi dan Manajemen) Vol 10 No 2 (2022): Jursima Vol. 10 No. 2, Agustus Tahun 2022
Publisher : INSTITUT TEKNOLOGI DAN BISNIS INDOBARU NASIONAL

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47024/js.v10i2.403

Abstract

AbstrakHampir dua tahun, dunia dihadapkan dengan adanya malasah virus mematikan yang dikenal dengan sebutan Coronavirus Disease 2019 atau disingkat Covid-19. WHO telah menetapkan masalah virus corona sebagai suatu pandemic global, pandemic ini telah mengganggu berbagai kegiatan tak terkecuali kegiatan Pendidikan. Kegiatan belajar mengajar di sekolah yang semula dilakukan dengan tatap muka, karena adanya pandemic ini berubah menjadi pembelajaran jarak jauh atau disebut dengan Dalam Jaringan (Daring).. Penelitian ini bertujuan akan Melakukan Penglompokan Hasil Belajar Siswa Pada Masa Covid-19 Dengan Algoritma K-Mean Untuk Menjamin Mutu Pendidikan Di Smk Bina Cendekia. Oleh Karena itu, metode yang akan digunakan penelitian ini adalah metode Algoritma K-Means Clustering. Dilakukan data mining terhadap dataset hasil belajar siswa. Selanjutnya dilakukan praprocessing terhadap dataset tersebut untuk menghilangkan data missing dan menentukan atribut-atribut data yang diperlukan untuk pengelompokkan. Untuk menentukan jumlah kelompok yang ideal maka dilakukan perhitungan nilai kelompok menggunakan Davis Bouldin Indeks serta menghitung distance performance, Penelitian ini menghasilkan pengelompokkan hasil belajar siswa pada masa pandemic covid-19 dengan menggunakan algoritma k-mens akan diperoleh jumlah kelompok sebanyak 2 Cluster Kelompok. Dimana nilai distance performance sebesar 74.166% diperoleh nilai DBI sebesar 0.669Keywords: Pengelompokan, Algoritma K-Means Clustering
Rancang Bangun Sistem Informasi Arsip Surat Menggunakan Metode Waterfall Pada Dinas Lingkungan Hidup Kota Cirebon Cep Lukman Rohmat; Dea Eryanti Putri; Martanto Martanto; Willy Prihartono
INFORMATICS FOR EDUCATORS AND PROFESSIONAL : Journal of Informatics Vol 7 No 2 (2023): INFORMATICS FOR EDUCATORS AND PROFESSIONAL : JOURNAL OF INFORMATICS (Juni 2023)
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat Universitas Bina Insani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51211/itbi.v8i1.2185

Abstract

Penggunaan teknologi informasi dan komputer sudah menjadi kebutuhan dalam pekerjaan sehari-hari, baik di instansi swasta maupun pemerintahan, terutama dalam hal pelayanan kepada masyarakat, penggunaan teknologi informasi tentunya sangat menunjang kegiatan yang ada. Salah satu kegiatan di Badan Pusat Statistik adalah banyaknya surat masuk dan surat keluar, hal ini menyebabkan permasalahan dalam pengarsipan baik surat masuk maupun surat keluar, maka untuk mengatasi hal tersebut dibuatlah sistem pengarsipan surat masuk dan surat keluar berbasis website. Mengingat arsip adalah segala bentuk rekaman kegiatan yang dilakukan oleh organisasi dan lembaga dalam berbagai bentuk, seperti dokumen, arsip dan sebagainya, maka keberadaannya perlu dipelihara dengan baik metode yang digunakan dalam pembuatan sistem filing adalah metode prototype, model ini mengambil pendekatan pengembangan sistem yang terdiri dari beberapa tahapan yaitu Communication, Quick plan, Modeling Quick Design, Pembentukan Prototype dan Deployment Delivery & Feddback yang akan meliputi bagaimana konsep, perencanaan, analisis dan perancangan apa saja yang dibutuhkan oleh sistem yang akan dibangun. Selain itu menggunakan PHP & MySQL sebagai bahasa pemrogramannya dengan framework codeigniter penelitian ini menghasilkan sistem pengelolaan arsip surat masuk dan surat keluar yang dapat menyimpan surat secara digital dan memudahkan setiap divisi untuk mencari surat dan membantu staff dalam mengelola surat,petugas administrasi dapat menambah, mengedit, menghapus, dan menyimpan surat masuk dan keluar, mencari berdasarkan nomor surat, subjek, pengirim, tujuan, dan tanggal, serta mengunduh surat digital berdasarkan hasil pencarian dengan mengakses aplikasi pemberkasan ini dengan user login yang tepat . Sistem pengarsipan surat yang telah dibangun dapat memudahkan pegawai dalam menyelesaikan pengolahan data arsip surat masuk dan surat keluar dengan cepat dan efisien.
Analisis Jaringan Akses Fiber to The Home Menggunakan Teknologi Gigabit Passive Optical Network Meida Nurus; Odi Nurdiawa; Martanto Martanto
Jurnal Janitra Informatika dan Sistem Informasi Vol. 3 No. 2 (2023): Oktober - Jurnal Janitra Informatika dan Sistem Informasi
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25008/janitra.v3i2.168

Abstract

Serat optic merupakan saluran transmisi atau jenis kabel yang terbuat dari kaca atau plastic yang sangat halus dan lebih kecil dari sehelai rambut, dan dapat digunakan untuk mentransmisikan sinyal cahaya dari suatu tempat ke tempat lain, sumber cahaya yang digunakan biasanya adalah laser atau LED. Teknologi fiber merupakan media yang tidak diragukan untuk menyediakan bandwidth yang besar, tidak dipengaruhi interferensi gelombang elektromagnetik, bebas korosi dan menyediakan rugi-rugi minimal untuk transportasi data. Sekarang ini kebanyakan dari backbone jaringan yang ada telah dikonstruksikan dengan fiber optik. Di dalam jaringan fiber optik masalah yang terjadi adalah perambatan cahaya yang kurang maksimal disebabkan oleh makrobending yang terjadi di sepanjang alur kabel atau jaringan. Kajian secara eksperimental untuk menganalisa redaman pada makrobending telah dilakukan. Pengambilan data dilakukan dengan mengukur perubahan nilai intensitas cahaya akibat adayanya gejala pembengkokan (bending) melalui perangkat Optical Power Meter. Penelitian dilakukan pada CV. Anugerah Tekhnik Pratama adalah penelitian asosiatif dengan menggunakan metode analisis kuantitatif. T-hitung sebesar 129.262, dan t-table sebesar 2.0057. Maka dapat ditarik kesimpulan bahwa T-hitung > dari T-table yakni sebesar 129.262> 2.0057. Hal tersebut berarti H0 diterima dan Ha ditolak  H0 : analisis jaringan akses fiber to the home (ftth) menggunakan teknologi gigabit passive optical network lebih besar dari 60 %.
Pelatihan Sistem Informasi Penjualan Rotan Bagi Pelaku Rotan Tegalwangi Martanto; Nana Suarna; Willy Prihartono
AMMA : Jurnal Pengabdian Masyarakat Vol. 2 No. 8 : September (2023): AMMA : Jurnal Pengabdian Masyarakat
Publisher : CV. Multi Kreasi Media

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The Tegalwangi rattan industry, as one of the traditional industries with high cultural value, faces various challenges in the era of globalization. One proposed solution is the training in the use of rattan sales information systems to enhance the competitiveness and efficiency of this industry. This Community Engagement aims to analyze the situation of the rattan industry in Tegalwangi, identify the challenges it faces, and design a community engagement approach to help address these issues. The training has brought significant benefits to rattan business operators in Tegalwangi. With the implementation of the Rattan Sales Information System, they can optimize the sales process, improve operational efficiency, and expand their market reach. This system also enables them to be more responsive to customer demands and needs. Additionally, training participants have gained a deeper understanding of information technology and how to apply it to their businesses. They have learned how to manage sales data more effectively, analyze sales trends, and make decisions based on accurate and up-to-date information. With this training, rattan business operators in Tegalwangi become more competitive in an increasingly competitive market. They can compete more effectively through the improvement of service quality and products, as well as adaptation to technological advancements.
Transformasi Digital Desa : Peningkatan Akses Pelayanan Masyarakat Melalui Pelatihan Aplikasi Di Desa Kali Deres Martanto; Fadhil Muhammad Bsysyar; Agus Bahtiar
AMMA : Jurnal Pengabdian Masyarakat Vol. 1 No. 09 (2022): AMMA : Jurnal Pengabdian Masyarakat
Publisher : CV. Multi Kreasi Media

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Kali Deres Village is one of the villages in our region that has great potential for development. Despite its potential, there are several challenges that need to be overcome, including less efficient access to public services for the community. Therefore, this project aims to build and implement a community service application that will increase the efficiency and accessibility of services for village residents. Kali Deres Village may have limitations in communication infrastructure such as slow or unstable internet networks. This can hinder the accessibility and use of the app. Villagers may not have enough technological knowledge or skills to use the app effectively. Adequate training is required to ensure correct use. As a result of this community service, training participants can increase their understanding of village information systems, including the use of related software and technology. Participants can develop or update their own village information systems by utilizing the training. By utilizing an appropriate village information system, operational processes can become more efficient and effective and a good village information system can enable the provision of better and faster public services to residents.
Prediksi Tingkat Kelulusan Mahasiswa Menggunakan Machine Learning dengan Teknik Deep Learning Martanto Martanto; Irfan Ali; Mulyawan Mulyawan
Jurnal Informatika: Jurnal Pengembangan IT Vol 4, No 2-2 (2019): Special Issue on Seminar Nasional - Inovasi Dalam Teknologi Informasi & Teknol
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v4i2-2.1877

Abstract

The graduation rate of students on time at the Informatics Engineering study program STMIK IKMI Cirebon greatly affects the accreditation assessment. Graduation prediction is difficult to do, but many have done predictions using a variety of methods. Graduation prediction is needed in order to determine preventive policies for students who graduate not on time. The method used in this research is Machine learning with deep learning techniques. The data set used as many as 405 data of students who graduated on time or who were not on time. The research attributes used are the Nim attribute, the GPA value of students who have graduated and the status of graduating or not graduating. The results of this study are the level of accuracy using Machine Learning by 72.84%.
Cluster Barang Elektronik Mengguanakan Algoritma Fuzzy C-Means dengan Optimize Parameter Grid Lutfi Hakim; Irfan Ali; Martanto Martanto
Jurnal Accounting Information System (AIMS) Vol. 6 No. 1 (2023)
Publisher : Ma'soem University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32627/aims.v6i1.695

Abstract

Electronic products are goods that are really needed at this time, because electronic goods really help humans in carrying out various daily activities, such as television, computers, cellphones, etc. The problem is how to apply the Fuzzy C-means method with Optimize Parameter Grid in the form of grouping electronic goods data for the needs of consumers used, and how to determine the optimum number of clusters from the use of the method used in grouping electronic goods data sets to find the best accuracy value. The purpose of this study was to apply the use of the fuzzy c-means algorithm in the case of grouping electronic data and produce an output to find out the best value of the electronic data used. While this research uses one method, namely the fuzzy cmeans algorithm with Optimize grid parameters which are included in the grouping rules in data mining. The research results are expected to be able to find the best electronic data set grouping based on the Davies Bouldin Index value resulting from the analysis of the fuzzy c-means algorithm at measure_type : NumericalMeasure, Dbi = 0.516 and measure_type : MixedMeasure, Dbi = 0.627.
Prediksi Jumlah Produksi Sablon Tahun Menggunakan Algoritma Regresi Linear di Nolbas SVNR Muhammad Fadhilah; Martanto Martanto; Irfan Ali
INTERNAL (Information System Journal) Vol. 6 No. 1 (2023)
Publisher : Masoem University

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Abstract

Nolbas svnr is a business engaged in the clothing industry which refers more to t-shirt screen printing. This business carries out its activities based on customer orders received through orders from individuals, shops, and schools. With the many types of screen printing that are made, the number of orders received and executed by Nolbas Svnr increases. Screen printing production at Nolbas Svnr is always changing every year. The main objective of this research is to obtain a predictive model for the amount of screen printing production using the Linear Regression method based on the number of orders obtained each year. The results that can be obtained in research can help for the supply of raw materials, the amount of raw materials, paint and so on. This study uses the linear regression method to process sales data using attributes such as year, customer name, price of goods, price of materials and the number of orders. of 0.5601. The results of the constant values ??and regression coefficients are used to predict the amount of screen printing production in 2023 at Zerobas SVNR and the predicted value is 3391. Evaluation of the multiple linear regression model shows an MAE value of 3.7247, an MSE value of 17.8633 and an R2 score of 87% .
Analisis Algoritma K-Nearest Neighbor terhadap Sentimen Pengguna Aplikasi Shopee Muhammad Saifurridho; Martanto Martanto; Umi Hayati
Jurnal Informatika Terpadu Vol 10 No 1 (2024): Maret, 2024
Publisher : LPPM STT Terpadu Nurul Fikri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54914/jit.v10i1.1054

Abstract

One way to gauge users' thoughts and sentiments towards a particular product, service, or subject is by conducting sentiment analysis on reviews posted on the Google Playstore platform. Among the plethora of apps available on the Google Playstore is Shopee. Due to the vast and unstructured nature of user comments in the review section, it becomes challenging to quickly and accurately grasp the overall information. This research aims to classify sentiments as positive, negative, or neutral, with the hope that the Shopee app can improve. Hence, the K-Nearest Neighbor Algorithm is employed to analyze sentiments to ensure users' opinions regarding their interaction with the Shopee program. Sentiment analysis is utilized to categorize reviews into positive, neutral, and negative groups. A dataset of 2000 entries is used in this analysis, obtained through web scraping, with 70% as training data and 30% as test data. The results indicate that this data split scenario yields the best model, achieving an accuracy of 70%, precision of 50.5%, recall of 44.8%, and an F1-score of 48.3% overall. To optimize results further, the implementation of more optimal data sampling techniques is necessary to attain a more balanced class distribution in both training and test data.
Klasifikasi Tumor Otak menggunakan Convolutional Neural Network dan Transfer Learning Muhammad Hasan Fadlun; Martanto Martanto; Umi Hayati
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol 6, No 1 (2024): Maret
Publisher : Universitas Wahid Hasyim

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36499/jinrpl.v6i1.10318

Abstract

Brain tumor is an uncontrolled growth of cells in the form of a mass or tissue within the brain, capable of producing both cancerous and non-cancerous symptoms. Brain tumors are part of a group of tumors involving the nervous system, including tumors in the spinal cord and peripheral nerves. It is not a common disease, and prompt intervention is necessary to receive timely medical treatment or appropriate therapy. This research aims to apply Deep Learning techniques in the automatic classification of brain tumors. In this study, a dataset of brain MRI images covering various types of brain tumors was used. The dataset consisted of 3264 MRI images with four classes: glioma, meningioma, pituitary, and no tumor, obtained from Kaggle.com. The system utilized a pre-trained CNN architecture, EfficientNet-B0, trained on the ImageNet dataset. In the Transfer Learning phase, fine-tuning was performed on the last layers of the CNN to adapt it to the brain tumor image dataset. The Convolutional Neural Network model was trained using MRI images to identify important features related to brain tumors. Subsequently, with Transfer Learning, the knowledge acquired by the pre-existing model was adopted and applied to a new dataset to enhance model performance. The application of Deep Learning techniques in the automatic classification of brain tumors provides significant benefits in medical practice. With this system, doctors and radiologists can obtain more effective assistance in diagnosis and treatment planning. The ability to automatically recognize brain tumors with high accuracy also enables the adoption of this technology in various medical facilities, thereby improving the accessibility of testing and treatment needed by patients. The results of this research demonstrate that the CNN and TL methods successfully achieved high performance, including an epoch accuracy of 0.9981 or 99%, a loss of 0.0061, and an evaluation with values generated by the confusion matrix showing high precision of 0.98 or 98%, recall of 0.98 or 98%, and an F1-score of 0.98 or 98%. This study illustrates the significant potential of implementing Deep Learning techniques, particularly CNN and TL, in the automatic classification of brain tumors. Advances in this field can contribute significantly to improving the diagnosis, treatment, and prognosis of brain tumor patients, accelerating efforts to address this complex disease.
Co-Authors A, Ronny Abdillah, Naufal Abdul Rosid, Rizal Ahmad Rifai Aji Dian Permana, Muhamad Aji Saputra, Mohammad AKBAR, MUHAMAD DENI Alfin Maulana Almadina, Muhammad Fitrian Shousyade Alpian Novansyah, Indi Andini, Eva Ardhanur, Ichlas Asmana, Asmana Augustian Pangestiazi, Irvanda Azahra, Amaliyah Putri Aziz Sahidin, Naufal Bernadeta Wuri Harini Cep Lukman Rohmat Chrisna Basila Rahman, Muhammad Damar Widjaja Darmanto Darmanto Dea Eryanti Putri Dewi Yuliyanti, Dewi Dian Ade Kurnia Dias Bayu Saputra Dikananda, Arif Rinaldi Dilita Pramasmawari Lita Dita Rizki Amalia Diyanti yanti Djoko Untoro Suwarno Dwi Hastuti, Ningrum Edy, Benediktus Yudha Fadhil Muhammad Bsysyar Faisal Adam, Faisal Faizal Rizqi, Muhammad Faroman Syarief, Faroman Fathur Rezki Junaedi, Muhammad fatimah, lilis Fauzan Afrizal, Ricky Febriani, Budi Febriyani, Adinda Fihir, Muhammad Fithriyani, Nurul Muna Fuji Astri, Dewanti Gifthera Dwilestari Hamam, Moh Hardika Hardika, Hardika Harini, BW Haryanto, Agustinus Surya Hayati , Umi Hayati, Umi Heliyanti Susana Hepsi Nindiasari Hidayat, Fajar Ignatius Adi Prabowo Ika Anikah Iksan Maulana, Muhammad Irfan Ali Irfan Ali, Irfan irfan cholid Iswanjono Iswanjono Jamaludin, Maulana Jamalul'ain, Abdul Kamil, Firmanilah Khoirunisa, Pitria Kholilullah, Mohammad khusnul khotimah Linggo Sumarno Lukmanul Hakim Lutfi Hakim Ma'arif Syaefullah, Muhammad Mahardika, Fathoni Maulana Jamaludin Maulana Yusuf, Muhammad Meida Nurus Mirna Mirna Moruk, Ewaldus Mu'min Azis, Muhammad Mubarok Mubarok Muhamad Djaelani Muhamad farhan Tholhah hidayat Muhamad Jihad Andiana Muhamad Taufik Sugandi Muhammad Aditya Rabbani Adit Muhammad Fadhilah Muhammad Haikal Muhammad Hasan Fadlun Muhammad Saifurridho Mujibulloh, Mujibulloh Mulyawan Mulyawan, Mulyawan Musyarofah Musyarofah, Musyarofah Muzani, Muhamad Muzilin, Elin Nailil Amani, Najiyah Nana Suarna Nanita, Nanita Nining Rahaningsih Nova Zulfahmi, A Nova Zulfahmi, A. Nur Asih, Nur Nur Hermawan, Ilham Nurhanifah, Indah Odi Nurdiawa Odi Nurdiawan Panca Wardanu, Adha Petrus Setyo Prabowo Prabowo, PS Prahara, Sukma Primawan, A.Bayu Puji Rahayu Putri, Niken Zeliana Raditya Danar Dana Ramdan Adi Surya, Muhamad Rifa'i, Ahmad Rifa’I, Ahmad Rinaldi Dikananda, Arif Rinaldi, Arif Riskandi, Muhammad Rizal Rizal Rizka Amelia Rohman, Dede Ronny Dwi Agusulistyo Saeful Anwar Safrudin, Muhamad Saifurridho, Muhammad Salsabila Ainal Wasilah, Qonita Samsudin, Risma'ruf Setiyani, Th. Prima Ari Setiyani, TPA Siti Paridah, Ninda Sri Suwartini Subur, Muhamad Sulistiyana Sulistiyana Sumarno, L Suryaningsih Suryaningsih Suwarno, DU Syahri, Ibnu Nava Syam Al ghifari, Muhammad Syamsul Aripin, Muhammad Syaripah, Imas Syifa, Nurkhasanah Fadhila Tati Suprapti Thomas Agam Tjendro Tri Anelia Tri Gustiane, Indri Tuti Hartati Umi Hayati Ummiyati Ummiyati W Widyastuti, W Wibowo, Daniel Widjaja, D Wihadi, Dwiseno WIHADI, RB DWISENO Willy Prihartono Wiwien Widyastuti Wujarso, Riyanto Yudhistira Arie Wijaya Zulfahmi, A. Nova