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All Journal Explore: Jurnal Sistem Informasi dan Telematika (Telekomunikasi, Multimedia dan Informatika) Abdimas Universal Jurnal Teknologi Informasi : Jurnal Keilmuan dan Aplikasi Bidang Teknik Informatika
Dimmas Mulya
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Law, Crime, Criminology & Criminal Justice Library & Information Science Social Sciences Other

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PENGEMBANGAN KETERAMPILAN GURU MELALUI PELATIHAN PENGELOLAAN MEDIA PEMASARAN BERBASIS DIGITAL Laser Narindro; Rully Mardjono; Dimmas Mulya; Elfira Febriani Harahap
Abdimas Universal Vol. 3 No. 2 (2021): Oktober
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat Universitas Balikpapan (LPPM UNIBA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36277/abdimasuniversal.v3i2.120

Abstract

To make labor for the Business and Industrial Sectoral in the industrial era 4.0 requires educational institutions such as schools to fulfill entrepreneurial-based curriculum achievements through the Entrepreneurial Maker School program which has been targeted by the Government to support creating graduates who has the soul, knowledge and skills in marketing products using digital media. To support this, one of the efforts is through training on making web-based marketing applications and digital marketing strategies for this teacher for using CMS tools in the form of wordpress applications and marketing strategies using Google Adsense tools to provide the needed understanding and skills. The purpose of this skills training is to help teachers be able to have innovations in teaching and learning activities and make reference to teaching materials that are given to students in class. This community service activity partner is the 40 Jakarta State Vocational High School. The methods used are lectures in providing materials and skills training for teachers. As for 20.8% of respondents quite understand, 50% of respondents understand and 29.2% of respondents really understand the material provided during the skills training provided.
PERBANDINGAN WAKTU EKSEKUSI PERAMALAN HARGA KOMODITAS PANGAN MENGGUNAKAN SPARKR DAN R STUDIO Dedy Sugiarto; Dimmas Mulya; Abdul Rochman; Is Mardianto
Jurnal Teknologi Informasi: Jurnal Keilmuan dan Aplikasi Bidang Teknik Informatika Vol. 16 No. 1 (2022): Jurnal Teknologi Informasi : Jurnal Keilmuan dan Aplikasi Bidang Teknik Inform
Publisher : Universitas Palangka Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47111/jti.v16i1.3911

Abstract

The arrival of the big data era with characteristics such as large volumes of data makes the calculation of execution time a concern when carrying out data analytics processes, such as forecasting food commodity prices. This study aims to examine the effect of the big data framework through the use of sparkR. The test is carried out by varying several deep learning forecasting models, namely the multi-layer perceptron model and by using the price of one food commodity from 2018 to 2020. The results show that sparkR is significantly shorter its execution time when compared to R studio. The results of testing the influence of the MLP model also show that a model with two hidden layers with a maximum node of 13 nodes in hidden layers 1 and 2 produces the longest execution time compared to only using 1 hidden layer with 5 nodes or using two hidden layers with a number of nodes of 5 and 3.
Visualisasi Kinerja dan Persepsi Peserta Program Bangkit 2021 Menggunakan Microsoft Power BI Dedy Sugiarto; Rianti Dewi Sulamet-Ariobimo; Binti Solihah; Ahmad Zuhdi; Ratna Shofiati; Anung Barlianto Ariwibowo; Teddy Siswanto; Dimmas Mulya
Explore: Jurnal Sistem Informasi dan Telematika (Telekomunikasi, Multimedia dan Informatika) Vol 13, No 1 (2022): Juni
Publisher : Universitas Bandar Lampung (UBL)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36448/jsit.v13i1.2311

Abstract

Penelitian ini bertujuan untuk membangun visualisasi kinerja dan persepsi peserta program Bangkit 2021 Fakultas Teknologi Industri Universitas Trisakti dalam bentuk dasbor. Data berasal dari respon kuesioner peserta Bangkit 2021 terkait dampak program MBKM, data transkrip mahasiswa yang diperoleh dari penyelenggaraan Program Bangkit dan data indeks prestasi mahasiswa yang didapatkan dari sistem informasi akademik (student information system) Universitas Trisakti. Pemodelan data menggunakan model skema bintang dengan tiga tabel fakta yaitu tabel nilai mata kuliah yang diikuti, tabel kehadiran dan status lulus serta tabel kuesioner. Tabel dimensi terdiri atas dimensi jalur pembelajaran, dimensi program studi, dimensi mata kuliah. Hasil visualisasi menunjukkan laporan kinerja dan persepsi peserta dapat dengan mudah dan singkat dilihat dalam masing-masing satu layar yang dapat disaring berdasarkan dimensi program studi, jalur pembelajran maupun mata kuliah yang diikuti. Secara umum 75% dinyatakan lulus penuh (full) dan 25 % lulus sebagian (parsial) serta salah seorang peserta berhasil mendapatkan predikat 50 tim terbaik. Seluruh peserta juga menyatakan kegiatan ini bermanfaat bagi mereka untuk meningkatkan keterampilan dan keahlian serta meningkatkan kemampuan bekerja sama dalam sebuah tim.
PERBANDINGAN KINERJA KLASIFIKASI SENTIMEN ULASAN PRODUK PEMBELIAN BERAS DI MARKETPLACE SHOPEE Dedy Sugiarto; Syandra Sari; Anung Barlianto Ariwibowo; Fitria Nabilah Putri; Dimmas Mulya; Tasya Aulia; Arviandri Naufal Zaki
Jurnal Teknologi Informasi: Jurnal Keilmuan dan Aplikasi Bidang Teknik Informatika Vol. 17 No. 1 (2023): Jurnal Teknologi Informasi : Jurnal Keilmuan dan Aplikasi Bidang Teknik Inform
Publisher : Universitas Palangka Raya

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

Abstract

This study aims to compare the performance of product purchase sentiment classification in market place shopee using four classification algorithms, namely support vector machine (SVM), naïve bayes (NB), logistic regression (LR),  k-nearest neighbor (KNN) and associated with the feature extraction model used, namely term frequency - inverse document. frequency (TF-IDF) and bag of word (BOW).   Data collection was carried out by extracting rice product review data through the Shopee website using a web scraping technique which was then saved in the form of a file with CSV format. The number of product reviews obtained is 3531 reviews and after pre-processing through the elimination of duplicate reviews, there are 464 reviews with details 16.17% having a negative label (rating 1 or 2), 15.52% having a neutral label (rating 3), and 68.32% have a positive label (rating 4 or 5). The composition of the rankings shows that the data is not balanced. The experimental results show that the combination of LR with TF-IDF shows the best performance with an accuracy of 80%.
Co-Authors Abdul Rochman Ahmad Zuhdi Anung B Ariwibowo Arviandri Naufal Zaki Binti Solihah Dedy Sugiarto Elfira Febriani Harahap Fitria Nabilah Putri Is Mardianto Narindro, Laser Ratna Shofiati Rianti Dewi Sulamet-Ariobimo Rully Mardjono Syandra Sari Tasya Aulia Teddy Siswanto