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All Journal SMATIKA JURSIMA (Jurnal Sistem Informasi dan Manajemen) Jurnal Teknik Mesin, Industri, Elektro dan Informatika JURSIMA
Martanto Martanto
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Industrial & Manufacturing Engineering Library & Information Science Social Sciences

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Pengembangan Media Pembelajaran Matakuliah Sistem Operasi Jaringan Berbasis Smartphone Martanto Martanto; Cep Lukman Rohmat
SMATIKA JURNAL Vol 11 No 02 (2021): SMATIKA Jurnal : STIKI Informatika Jurnal
Publisher : LPPM STIKI MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/smatika.v11i02.623

Abstract

Students who registered at STMIK IKMI Cirebon in the last 3 years showed that 51% of applicants were from SMK graduates. Even though he comes from a vocational school, it is still very difficult to understand how to start learning about network operating systems. This is evident from the results of the Network Operating System lectures where only 1-2 people pass in each class. The use of smartphone media is expected to make it easier for lecturers to convey learning material so that the pattern of student center learning activities can run better without knowing time which means that students as learning objects will be able to better understand the material well. This study uses the Analysis, Design, Development, Implementation and Evaluation (ADDIE) development model. This research is divided into 5 stages, namely: the first stage the researcher conducts the analysis, there are two stages of analysis, namely performance analysis and needs analysis, the second stage the researcher makes a system design design which includes learning objectives, assessment instruments, exercises, content, subject matter analysis, lesson planning and media selection, the third stage the researchers began to create a system which includes the activities of making, buying, and modifying teaching materials to achieve the goals of the learning that have been determined, the fourth stage the researcher applies the system that has been created, the data generated in the application will be used for further improvement processes, In the fifth stage, the researcher evaluates the data that can be obtained at the previous stage, whether or not improvements are needed in the system that has been made. The results of the User Acceptance Test (UAT) in this study indicate that the learning media for the smartphone-based network operating system course is quite good because it has a percentage value that is above 70% on average.
KLASIFIKASI MOTIF BATIK JAWA MENGGUNAKAN ALGORITMA K-NEAREST NEIGHBORS (KNN) MUHAMAD DENI AKBAR; Martanto Martanto; Yudhistira Arie Wijaya
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.v10i2.412

Abstract

Batik is one of Indonesia's beautiful and well-known heritages throughout the world, batik as a traditional heritage of the archipelago comes with a variety of motifs. Each region has different motifs and different philosophies. The number of Indonesian batik motifs spread from Aceh to Papua, so not everyone can distinguish batik motifs. This study aims to distinguish between Javanese batik motifs and non-Javanese batik motifs. The batik motifs taken by researchers as samples from the Java region were the Mega Mendung batik motif, Lasem batik motif, Sekar Jagad batik motif, Kawung batik motifs and motifs, and for non-Javanese researchers took samples of Cendrawasih batik motifs, Dayak batik motifs, and batik motifs. nutmeg, and Balinese batik motifs. The research method uses the K-Nearest Neighbors (KNN) algorithm which has stages of collecting image on batik motifs, knowing Javanese and non-Javanese batik motifs, pre-processing, feature extraction, image classification, and evaluation of motifs. Color feature extraction is carried out using gray level co-occurrence matrix (GLCM) methods. Based on the test results show that with the application of GLCM and KNN with an image size ratio of 200x200 with a ratio of k=5 the percentage of split image 80% test and 20% training is able to produce an accuracy of 65%.
KLASIFIKASI MOTIF BATIK JAWA MENGGUNAKAN ALGORITMA K-NEAREST NEIGHBORS (KNN) MUHAMAD DENI AKBAR; Martanto Martanto; Yudhistira Arie Wijaya
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.v10i2.412

Abstract

Batik is one of Indonesia's beautiful and well-known heritages throughout the world, batik as a traditional heritage of the archipelago comes with a variety of motifs. Each region has different motifs and different philosophies. The number of Indonesian batik motifs spread from Aceh to Papua, so not everyone can distinguish batik motifs. This study aims to distinguish between Javanese batik motifs and non-Javanese batik motifs. The batik motifs taken by researchers as samples from the Java region were the Mega Mendung batik motif, Lasem batik motif, Sekar Jagad batik motif, Kawung batik motifs and motifs, and for non-Javanese researchers took samples of Cendrawasih batik motifs, Dayak batik motifs, and batik motifs. nutmeg, and Balinese batik motifs. The research method uses the K-Nearest Neighbors (KNN) algorithm which has stages of collecting image on batik motifs, knowing Javanese and non-Javanese batik motifs, pre-processing, feature extraction, image classification, and evaluation of motifs. Color feature extraction is carried out using gray level co-occurrence matrix (GLCM) methods. Based on the test results show that with the application of GLCM and KNN with an image size ratio of 200x200 with a ratio of k=5 the percentage of split image 80% test and 20% training is able to produce an accuracy of 65%.
Sistem Informasi Persedian Barang Pada Badan Pusat Statistik Kab.Kuningan Ayu Sulastini; Martanto Martanto
Jurnal Teknik Mesin, Industri, Elektro dan Informatika Vol. 2 No. 1 (2023): Maret : JURNAL TEKNIK MESIN, INDUSTRI, ELEKTRO DAN INFORMATIKA
Publisher : Pusat Riset dan Inovasi Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jtmei.v2i1.1301

Abstract

Kantor badan pusat statistik kuningan adalah satu lembaga resmi pemerintah yang melakukan banyak survey. Untuk melakukan survey dibutuhkan perlengkapan lapangan seperti tas, rompi, aalat tulis kantor, papan untuk menulis, name tag dan masih banyak barang-barang yang dibutuhkan untuk lenacaran proses survey. Kebutuhan barang masuk dan keluar masih dicatat pada sebuah buku. Terkadang barang yang ada di catatan tidak sesuai dengan stok barang yang ada. Informasi ketersediaan barang juga msih terbatas.tujuan dari penelitian ini adalah untuk merancang bangun sistem persedian barang pada kantor badan pusat statistik kuningan. Metode yang digunakan dalam pembuatan sistem adalah metode prototype. Tahapan dalam penelitian ini pertama adalah analisis kebutuhan, pada tahap ini peneliti mendefinisikan kebutuhan sistem dengan rinci. Dalam prosesnya, klien dan tim developer akan bertemu untuk mendiskusikan detail sistem seperti apa yang diinginkan oleh user. Tahap kedua adalah desain cepat, pada tahap ini peneliti membuat desain sederhana yang akan memberi gambaran singkat tentang sistem yang ingin dibuat. Tahap ketiga adlaah membangun prototipe, pada tahap ini peneliti membangun prototipe sebenarnya yang akan dijadikan rujukan tim programmer untuk pembuatan program atau aplikasi. tahap keempat adalah evaluasi penggunaan awal, pada tahap ini peneliti melakukan presentasi pada klient untuk dieavaluasi. Tahap kelima adalah memperbaiki prototipr, pada tahap ini peneliti memperbaiki sistem jika ditemukan ketidak sesuaian dengan keinginan klien pada tahap ke empat, jika tidak ada lanjut ketahap keenam. Pada tahap keenam adalah implementasi dan pemeliharaan, pada tahap ini peneliti segera membuat sistem susai dengan prototipe akhir yang telah disepakati, kemudain peneliti pelakukan pengujian dan setelah selesai sistem diserahkan ke klien. Hasil dari penelitian ini adalah sistem inventory barang pada kantor badan pusat statistik kuningan.
Co-Authors Ayu Sulastini Cep Lukman Rohmat MUHAMAD DENI AKBAR Yudhistira Arie Wijaya