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LP3M Universitas Mulia Jl. Letjen Z.A. Maulani No. 9 Kelurahan Damai Bahagia Kecamatan Balikpapan Selatan Kota Balikpapan Provinsi Kalimantan Timur Indonesia
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METIK JURNAL
Published by Universitas Mulia
ISSN : 24429562     EISSN : 25801503     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Electrical & Electronics Engineering
Media Teknologi Informasi dan Komputer (METIK) Jurnal adalah jurnal teknologi dan informasi nasional berisi artikel-artikel ilmiah yang meliputi bidang-bidang: sistem informasi, informatika, multimedia, jaringan serta penelitian-penelitian lain yang terkait dengan bidang-bidang tersebut. Terbit dua kali dalam setahun bulan Juni dan Desember.
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 243 Documents
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Analisis Media Pembelajaran Berbasis E-Learning Munggunakan Metode Analytical Hierarchy Process Dini Oktaria Yusanto; Tri Santoso; Syahriani Syahriani
METIK JURNAL Vol 7 No 1 (2023): METIK Jurnal
Publisher : LPPM Universitas Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47002/metik.v7i1.429

Abstract

Berdasarkan berita yang terdapat didalam situs web kemdikbud.go.id, pandemi memang berdampak luar biasa bagi kehidupan manusia, termasuk aspek pendidikan. Karena hal itu, berbagai kebijakan dibuat oleh pemerintah untuk menahan lajunya penyebaran virus. Salah satu kebijakan yang diterapkan adalah penundaan sementara dalam kegiatan belajar di sekolah maupun dilingkup perguruan tinggi. Selain itu, pemerintah juga memberikan solusi berupa alternatif pembelajaran secara daring. Pembelajaran secara daring ini sangat dianjurkan, agar para siswa maupun mahasiswa dapat tetap mengikuti kegiatan belajar seperti biasanya. Dari permasalahan tersebut, aplikasi pembelajaran online menjadi sangat populer dan sangat diminati keberadaannya. Beberapa aplikasi pembelajaran online yang dibahas dalam penelitian ini adalah Ruang Guru, Schoology, Moodle, Rumah Belajar dan Google Classroom. Penelitian ini menggunakan metode Analytical Hierarchy Process (AHP) terhadap ke-5 aplikasi pembelajaran daring tersebut. Data-data yang diperoleh untuk melakukan penghitungan berasal dari kuesioner yang telah diisi oleh para responden. Kemudian data tersebut dioleh dengan menggunakan aplikasi Ekspert Choice. Setelah dilakukan penghitungan atas penelitian tersebut, maka diperolehlah hasil yang akurat dan objektif. Berdasarkan penghitungan yang dilakukan dengan menggunakan metode AHP, aplikasi belajar online Ruang Guru menempati posisi teratas yang diminati dengan skor 0.212. Selanjutnya pada posisi kedua ditempati oleh Google Classroom dengan skor 0.206, posisi ketiga Schoology dengan skor 0.202, pada posisi keempat yaitu Rumah Belajar dengan skor 0.192 dan terakhir adalah Moodle dengan skor 0.188.
Penerapan Fuzzy Times Series dan Regresi Linier dalam Melihat Stok Ketersediaan Beras Sayed Fachrurrazi; Angga Pratama; Syukriah Syukriah; Veri Ilhadi
METIK JURNAL Vol 7 No 1 (2023): METIK Jurnal
Publisher : LPPM Universitas Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47002/metik.v7i1.561

Abstract

Salah satu komoditas terbesar Indonesia adalah beras. Dimana Perum Bulog berperan penting dalam menyediakan cadangan beras bagi negara untuk menjaga stabilitas nasional Menjelang panen raya, Perum Bulog harus menyusun strategi stok beras yang terencana dengan baik agar tetap tersedia. Karena harga pasar beras yang tinggi akan memicu ekspor beras dari luar negeri, maka harga gabah bisa turun saat petani panen raya akibat ekspor beras dari luar negeri jika pasokan beras Bulog menjadi langka. Ketika permintaan pasar akan beras tidak dapat dipenuhi, biasanya muncul persoalan ini. Penelitian ini dilakukan untuk meramalkan produksi beras nasional untuk kepentingan. Data yang digunakan dalam penelitian ini adalah data produksi stok beras nasional yang diperoleh dari bps.id dari tahun 2018 sampai dengan tahun 2021. Dimana uji harga beras kualitas premium untuk AFER sebesar 0,74444% dan RMSE sebesar 8,9422. Peramalan harga beras kualitas sedang nilai error AFER sebesar 0,22927% dan RMSE sebesar 1,732. Dan pengujian beras tidak bermutu diperoleh nilai error AFER sebesar 0,23640 dan RMSE sebesar 09,09439. Pengujian dengan menggunakan metode regresi linier diperoleh hasil peramalan kualitas beras premium; kualitas menengah dan luar memiliki hasil peramalan yang sama dengan nilai 87,62%, dengan demikian forecasting untuk harga beras sangat baik. Selanjutnya terlihat bahwa nilai akurasinya diatas 80% yang sangat tinggi dengan melalui pengujian.
Implementasi Algoritma C5.0 Pada Klasifikasi Status Gizi Ibu Hamil di Kota Lhokseumawe Ilham Sahputra; Mauliza Mauliza; Siti Fatimah A Zohra
METIK JURNAL Vol 7 No 1 (2023): METIK Jurnal
Publisher : LPPM Universitas Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47002/metik.v7i1.562

Abstract

Status gizi ibu hamil dapat mempengaruhi pertumbuhan janin yang sedang dikandung, sehingga penentuan status gizi bagi ibu hamil menjadi sangat penting agar seorang ibu dapat menyesuaikan kondisi kesehatannya dengan baik. Penelitian ini bertujuan untuk menerapkan algoritma C5.0 pada klasifikasi status gizi ibu hamil. Penelitian ini menggunakan 355 dataset ibu hamil yang diperoleh dari beberapa puskesmas di kota Lhokseumawe. Penelitian ini bertujuan untuk menerapkan algoritma C5.0 untuk melakukan klasifikasi status gizi ibu hamil berdasarkan data yang diperoleh dari beberapa puskesmas yang berada di kota Lhokseumawe. Data primer yang diperoleh dari lapangan sebanyak 355 data yang terdiri dari 9 feature diantaranya: Umur, BB Dulu, BB Sekarang, TB, LiLA, Tekanan Darah, HB, IMT, dan BB Ideal. Sementara kelas data terdiri dari 2 kategori yaitu: gizi kurang dan gizi normal. Berdasarkan penelitian yang dilakukan, diperoleh hasil bahwa metode C5.0 mampu bekerja dengan sangat baik dalam melakukan klasifikasi data status gizi ibu hamil. Akurasi yang dihasilkan mencapai 94,33%, presisi 95%, recall 97%, dan f-1 score 96% dengan perbandingan jumlah data latih dan data testing adalah 70 : 30.
Analisis Dan Perancangan Sistem Informasi Penilaian Harga Perolehan Barang Dagang Dengan Metode Moving Average Qudratul Nurjanah, Agustin
METIK JURNAL Vol 7 No 2 (2023): METIK Jurnal
Publisher : LPPM Universitas Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47002/metik.v7i2.222

Abstract

The Indrayana Jaya Outdoors store requires an accurate and timely information system for the valuation of the purchase price of merchandise so that the flow of information needed runs as expected. The method of determining the moving average price (Moving Average), the acquisition price is not carried out at the end of the period, but at every purchase transaction. The results of the problem analysis found many things that caused the goals of the system not to be achieved. In knowing the information on the inventory of merchandise in the warehouse, it takes a duration of more than 10 minutes because you must first find and collect the required data one by one. Recording data that is still in handwriting or using working papers also hinders officers from finding the information needed because the document is not legible or multiple writing errors are made. As well as to determine the selling price, the purchase price of the goods is only an estimate, so the possibility of receiving only a small profit. By using the Moving Average Method, Indrayana Jaya Outdoors Stores can process data and present information on the acquisition price of merchandise accurately and on time.
Analisis DistilBERT dengan Support Vector Machine (SVM) untuk Klasifikasi Ujaran Kebencian pada Sosial Media Twitter Azmi Verdikha, Naufal; Habid, Reza; Johar Latipah, Asslia
METIK JURNAL Vol 7 No 2 (2023): METIK Jurnal
Publisher : LPPM Universitas Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47002/metik.v7i2.583

Abstract

Hate speech is a significant issue in content management on social media platforms. Effective classification of hate speech plays a crucial role in maintaining a safe social media environment, combating discrimination, and protecting users. This study evaluates a hate speech classification model using SVM with linear and polynomial kernels. The dataset used consists of labeled Indonesian-language tweets. The importance of developing an effective classification model to address hate speech has led to the utilization of DistilBERT as a feature extraction method. However, DistilBERT has high-dimensional features, necessitating dimensionality reduction to reduce model complexity. Therefore, in this study, the PCA dimensionality reduction method is implemented with various scenarios of dimensionality, namely 10, 20, 30, 40, and 50. Evaluation is performed using F1-Score, and the entire study is evaluated using 10-fold cross-validation. The evaluation results indicate that in the scenario with a linear kernel, the model achieves the highest F1-Score of 0.75 in the 50-dimensional scenario. Meanwhile, in the scenario with a polynomial kernel, the model achieves the highest F1-Score of 0.7857 in the 50-dimensional scenario. These findings demonstrate that the use of a polynomial kernel with 50 dimensions yields the best performance in classifying hate speech.
Implementasi Algoritma K-Means Untuk Mengelompokkan Mahasiswa Program Studi Pendidikan Matematika Berdasarkan Sumber Belajarnya Rizki, Nanda Arista; Kurniawan, Kurniawan; Hasan, Isran K.; Sampe, Nofia
METIK JURNAL Vol 7 No 2 (2023): METIK Jurnal
Publisher : LPPM Universitas Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47002/metik.v7i2.584

Abstract

Students must be able to utilize learning resources properly to improve academic achievement. Students can be grouped based on the learning resources they use frequently. Grouping results are helpful for lecturers in designing, evaluating, and analyzing learning in the classroom. This research aimed to implement the K-Means algorithm to classify student learning resources and determine which learning resources determine which groups. The population of this research were students of the Mathematics Education study program at Mulawarman University who are still taking courses. At the same time, the sample were active students from classes 2019, 2020, 2021, and 2022 of the Mathematics Education Study Program at Universitas Mulawarman who were still taking courses and were willing to fill out the questionnaire, namely as many as 111 Students. The data analysis used was clustering analysis using the K-Means algorithm with the Elbow method. New dummy data was formed from learning resource data because it was multiple choice. Based on the results, three main groups were obtained according to the use of learning resources. The learning resources that determine the distribution of groups were electronic books and journals. The first group used electronic books and journals, while the third group did not use either. While the second group only used electronic books. The Silhouette value for this cluster model was 0.615. The classification was classified as good.
Analisis Perbandingan Metode Decision Tree Dan K-Nearest Neighbor Untuk Klasifikasi Cyberbullying Pada Sosial Media Twitter Maradona, Maradona; Kusrini, Kusrini; Alva Hendi Muhammad
METIK JURNAL Vol 7 No 2 (2023): METIK Jurnal
Publisher : LPPM Universitas Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47002/metik.v7i2.591

Abstract

This research focuses on analyzing the impact of social media on society, particularly addressing the issue of cyberbullying on the Twitter platform. Based on statistics, the majority of internet users in Indonesia actively utilize social networks, with Twitter being the most dominant platform used for communication and interaction. Therefore, cyberbullying cases often occur on this social media platform. In this study, two classification methods, namely Decision Tree and K-Nearest Neighbor (KNN), were employed to classify cyberbullying-related messages on Twitter. The aim of this research is to compare the performance of these two methods and to identify early signs of cyberbullying as relevant digital evidence for legal proceedings. The dataset used in this study consists of 650 comment records from the period 2019 to 2021, with predefined labels. The analysis results indicate that K-Nearest Neighbor achieved the highest accuracy, reaching 75.99%, compared to Decision Tree with 65.00%. Hence, K-Nearest Neighbor is considered a more effective method for cyberbullying analysis on the Twitter platform. Additionally, the identification of early signs of cyberbullying in comment id 2 can serve as relevant digital evidence for legal purposes. This research provides better insights into the effectiveness of classification in addressing cyberbullying issues on the Twitter platform.
Implementasi Framework Streamlit Sebagai Prediksi Harga Jual Rumah Dengan Linear Regresi Syafarina, Gita Ayu; Zaenuddin, Zaenuddin
METIK JURNAL Vol 7 No 2 (2023): METIK Jurnal
Publisher : LPPM Universitas Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47002/metik.v7i2.608

Abstract

This research aims to develop an Artificial Intelligence (AI)-based application using the Streamlit framework to predict house sale prices in Banjarmasin City using Linear Regression methodology. The increase in demand and supply of properties in Banjarmasin City poses a complex challenge in determining house sale prices. The Linear Regression method was chosen as the primary analytical tool to identify factors influencing house sale prices. This application utilizes historical data of house sale prices and variables such as land area, building area, number of rooms, proximity to public facilities, and geographical location as inputs for the Linear Regression model. Furthermore, the Streamlit framework is employed to create an interactive and user-friendly interface for end-users. The outcome of this research is an AI application that assists potential buyers or sellers in Banjarmasin City in determining competitive prices. By inputting information about the property being evaluated, users can obtain a more accurate estimated sale price based on factors identified by the Linear Regression model. In testing the application, actual house sale price data from Banjarmasin City was used to assess the model's accuracy. The testing results indicate that the application is capable of providing reasonably accurate price estimates, achieving an accuracy level of 67.8%. Thus, this AI application holds the potential to be a valuable tool in the property industry in Banjarmasin City, aiding stakeholders in more informed and data-driven decision-making regarding house sale prices. Additionally, this application could serve as a foundation for further developments in AI research and property price analysis.
Analisis Cara Kerja Sistem Deteksi Infeksi Worm Pada Komputer Sumarno, Sumarno
METIK JURNAL Vol 7 No 2 (2023): METIK Jurnal
Publisher : LPPM Universitas Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47002/metik.v7i2.636

Abstract

Worms are a type of malware that has the ability to develop and spread automatically to other computers on a network without human interaction. This fast and undetectable infection capability makes worms a serious threat in the world of computer security. This research aims to explore the mechanisms and behavior of worm infection systems on computers. This study involves an in-depth analysis of the functions and methods used by worms to enter, infect, and exploit target computers. This researchalso explain how worms can cause damage to computer systems, steal confidential information, or even create botnet networks to carry out large-scale attacks. Research methods include collecting data from existing worm detection systems, analyzing system logs that occur, as well as simulations to understand how worms work in various scenarios. In addition, this research also consider protection and prevention techniques that can be used to protect computers and networks from worm attacks. Based on observations and experiments, the results of this research can be concluded that the worm infection system spreads through computers connected to the network or through other media on the network and does not require a certain moment to be a trigger to infect the target. It is hoped that the results of this research provide an in-depth understanding of worm infection systems on computers, allowing researchers and computer security practitioners to develop more effective protection strategies. Prevention and early detection efforts will be key in dealing with the growing threat from worms and similar types ofmalware.
Aplikasi Rekomendasi Pemesanan Paket Wisata Menggunakan Metode Collaborative Filltering Ibrahim Asad; Muhhamad Zakariyah
METIK JURNAL Vol 7 No 2 (2023): METIK Jurnal
Publisher : LPPM Universitas Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47002/metik.v7i2.639

Abstract

The advancement of information technology has led to the rapid growth of tourism information, causing difficulties for tourists to find relevant information according to their needs. Recommendation systems are one of the solutions to recommend tourist destinations based on user preferences. Tourism has become an industry that provides significant benefits to a region. Therefore, tourist attractions need to be developed to achieve maximum results. There are various impacts of tourism development, one of which is the improvement of the local economy in tourist areas. Yogyakarta is one of the cities with various tourist attractions and is a popular destination for people living in Central Java, specifically. However, the abundance of tourist destinations poses a challenge for tourists in making decisions. Tourism recommendations are made based on various factors, such as ticket prices, the distance of the tourist destination from the user's current location on maps, and facilities. The technique used is Collaborative Filtering (CF). Using this technique can provide accurate recommendations to each user. In this research, the Collaborative Filtering method is used to build a recommendation system by finding similarities among users.

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