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Jurnal Informatika: Jurnal Pengembangan IT
Published by Politeknik Harapan Bersama Tegal
ISSN : 24775126     EISSN : 25489356     DOI : https://doi.org/10.30591
Core Subject : Science,
Computer Science & IT
The scope encompasses the Informatics Engineering, Computer Engineering and information Systems., but not limited to, the following scope: 1. Information Systems Information management e-Government E-business and e-Commerce Spatial Information Systems Geographical Information Systems IT Governance and Audits IT Service Management IT Project Management Information System Development Research Methods of Information Systems Software Quality Assurance 2. Computer Engineering Intelligent Systems Network Protocol and Management Robotic Computer Security Information Security and Privacy Information Forensics Network Security Protection Systems 3. Informatics Engineering Software Engineering Soft Computing Data Mining Information Retrieval Multimedia Technology Mobile Computing Artificial Intelligence Games Programming Computer Vision Image Processing, Embedded System Augmented/ Virtual Reality Image Processing Speech Recognition
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 431 Documents
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Implementasi Aplikasi Sentimen Pada Data Twitter Jelang Pemilu 2024 Humam, Choirul; Laksito, Arif Dwi
Jurnal Informatika: Jurnal Pengembangan IT Vol 8, No 2 (2023)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v8i2.5051

Abstract

Elections are one of the most important democratic processes, giving citizens the right to choose their leaders. In today's digital era, social media is an increasingly important information source influencing public perception. Twitter has been a social media from the past until now that still exists in finding information. Tweets are one of the most frequently used services to express opinions or opinions to the public. Sentiment analysis as an application of Natural Language Processing (NLP) is helpful in understanding public opinion towards prospective leaders and issues discussed during election campaigns. The motivation for this study is to conduct text classification using a deep learning model called LSTM and to compare the use of oversampling and non-oversampling methods. This research started by collecting datasets from Twitter, labelling, pre-processing, creating and evaluating the model, and implementing it into the web application. The experiment showed that the random oversampling technique gets more significant accuracy than non-oversampling. Random oversampling produces an accuracy of 0.82 at epoch 25, while non-oversampling reaches an accuracy of 0.61 at epoch 50
Pengukuran Indeks Kepuasan Masyarakat Terhadap Pelayanan Dan Kualitas Data Penginderaan Jauh Hedy Izmaya; Dety Purnamasari
Jurnal Informatika: Jurnal Pengembangan IT Vol 6, No 2 (2021): JPIT, Mei 2021
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v6i2.497

Abstract

Technology and Data Center (Pustekdata) LAPAN in 2016 has distributed remote sensing satellite data of 86,176 scenes to 323 government institutions. In 2016, the data service has also achieved the quality management standard ISO 9001: 2015 and the value of the Community Satisfaction Index (IKM) which reached 82 with a very satisfactory predicate. To maintain and improve performance in data services to users, it is necessary to study the needs of users for the required remote sensing data, namely by conducting a survey to users of remote sensing data, especially High Resolution Satellite Imagery (CSRT). Eight indicators are used to measure community satisfaction from the 14 indicators of the Community Satisfaction Index in accordance with the Decree of the Minister of State Apparatus Empowerment Number: KEP/25/M.PAN/2/2004 concerning Guidelines for the Preparation of IKM Service Units of Government Agencies including BUMN/BUMD. The population in this study is all data buyers from 2017, 2018 and 2019. The survey data are then reviewed and analyzed using statistical analysis with instrument refinement by statistically validating the survey data. The 2017 Community Satisfaction Index scored 90.69% with the predicate "Very Good". In 2018 there was an increase of 18.41% with a value of 93.69% obtaining the "Very Good" predicate. In 2019, with a total of 516 respondents, an increase of 3.12% was obtained, a score of 96.81% was obtained with the predicate "Very Good".
Perbandingan Akurasi Euclidean Distance, Minkowski Distance, dan Manhattan Distance pada Algoritma K-Means Clustering berbasis Chi-Square M Nishom
Jurnal Informatika: Jurnal Pengembangan IT Vol 4, No 1 (2019): JPIT, Januari 2019
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v4i1.1253

Abstract

In data mining, there are several algorithms that are often used in grouping data, including K-Means. However, this method still has several disadvantages, including the problem of the level of accuracy of the methods used to measure the similarities between the objects being compared. To overcome this problem, in this study a comparison was made between three methods (euclidean distance, manhattan distance, and minkowski distance) to determine the status of disparity in Teacher's needs in Tegal City. The results showed that of the three methods compared had a good level of accuracy, which is 84.47% (for euclidean distance), 83.85% (for manhattan distance), and 83.85% (for minkowski distance). In addition, this study also informs that there are still 6 (six) schools with conditions that are very poorly available for teachers (in the category of HIGH disparity labels) and need to get more attention, which is SMP Atmaja Wacana, SMKN 3 Tegal, SMAS Muhammadiyah, SMAS Pancasakti Tegal, SMKS Muhammadiyah 1 Kota Tegal, and SMP IC Bias Assalam.
Aplikasi Model Sistem Dinamik Untuk Perencanaan Pembangkit Listrik Tenaga Air Dalam Rangka Memenuhi Kebutuhan Supply Dan Demand Energi Listrik Di Kepulauan (Studi Kasus: Pulau Madura) Addin Aditya; Erma Suryani
Jurnal Informatika: Jurnal Pengembangan IT Vol 3, No 1 (2018): JPIT, Januari 2018
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v3i1.649

Abstract

One of the energy problems in Indonesia is uneven electrification ratio. According to PT. PLN (Persero) East Java Distribution, Madura Island has the lowest electrification ratio in East Java, which is 59.02%. At present, Madura gets their electricity supply from Java Island through 2 high voltage sea cable circuit 150.000 volts with 2x100 MW capacity. However, that’s not enough to fulfill the Madura electricity demand. This research aims to develop a dynamic model of the hydroelectric power system in order to increase Madura’s electrification ratio. In this research, we use a dynamic system model to analyze both technical and economical aspect of developing the hydroelectric power system. A dynamic system model approach is a part of the concept which means it articulate the problem as a comprehensive system and relation between each element of it. We hope this research can pull the trigger of energy independence which is correspond with local resource especially in the eastern island of Indonesia.
Penilaian Kredit Menggunakan Algoritma XGBoost dan Logistic Regression Yaqin, Ainul
Jurnal Informatika: Jurnal Pengembangan IT Vol 8, No 1 (2023)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v8i1.4337

Abstract

Penilaian kredit merupakan suatu proses atau sistemyang digunakan oleh lembaga pembiayaan atau bank untukmenilai kelayakan seseorang yang mengajukan pinjaman. Halini sangat diperlukan untuk menghindari kerugian akibat gagalbayar. Menanggapi hal tersebut dibutuhkan sebuah metodeyang efisien, cepat dan akurat untuk mengklasifikasikan layakatau tidaknya seseorang untuk diberikan pinjaman. Penulismengusulkan metode machine learning dan membandingkanalgoritma XGBoost dan logistic regression. Setelah dilatih dandiuji dengan stratified kfold cross validation, XGBoostmenghasilkan rata-rata akurasi 85,51%; F1 Score 83,81%;precision 83,80% dan recall 84,04% sedangkan logisitcregression menghasilkan rata-rata akurasi 85,94%; F1 Score85,36%; precision 80,08%; dan recall 91,52%. Kedua algoritmadapat mengklasifikasikan layak atau tidaknya seseorang untukdiberikan pinjaman dengan baik, sehingga dapat digunakanuntuk membantu institusi keuangan maupun para analis kredit.
Klasifikasi Penyakit Tanaman Bawang Merah Menggunakan Metode SVM dan CNN Zalvadila, Alya
Jurnal Informatika: Jurnal Pengembangan IT Vol 8, No 3 (2023)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v8i3.5341

Abstract

Shallots are one of the most widely produced crops in Enrekang Regency. The obstacle in cultivation is the presence of disease in the plant which can reduce production yields. We can recognize this disease from the spots on the leaves because these spots have unique color and texture characteristics. The aim of this research is to determine the results of the classification of shallot plant diseases which focuses on purple spot and moler disease. The classification algorithms used are CNN and SVM with RBF, linear, sigmoid and polynomial kernels. The feature extraction method used is Gray Level Co-occurance Matrix (GLCM). The analysis was carried out using 320 datasets with 2 classes, namely, purple spot disease and moler disease, each class has 160 datasets. The test results show that the CNN and SVM methods with RBF, linear and polynomial kernels get accuracy, precision, recall and F1 scores of 100% respectively. Meanwhile, the SVM method on the sigmoid kernel using texture feature extraction with the GLCM method states that the accuracy value is 75%, precision 75%, recall 73% and F1-Score 74%. So these results state that the Sigmoid method using GLCM feature extraction has the lowest value among other methods
REPLACE SYSTEM ANDROID STANDARD PUSH CUSTOM ROM ZNXT6 Achmad Yuni Puspito; Oman Somantri
Jurnal Informatika: Jurnal Pengembangan IT Vol 1, No 2 (2016): JURNAL INFORMATIKA
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v1i2.397

Abstract

System standard pada operating system android masih dianggap kurang menarik dan maksimal, ini dapat dilihat dari segi tampilan, setingan, performance, sistem hingga akses yang dibatasi oleh vendor, sehingga dibutuhkanlah sebuah replace system android pada sebuah perangkat smartphone yang masih menggunakan system standard dengan menggunakan custom rom, android standard ditingkatan kinerja dan optimalisasi agar user atau pengguna lebih maksimal menggunakan perangkat android. Replace System Android Standard Push Custom Rom menggunakan system znxt 6 dikarenakan fitur yang lebih baik, seperti media apps, Home, System UI, dan dapat menikmati fitur bravia engine 2. Dengan penggantian custom rom kinerja dari perangkat dirasakan meningkat berbeda dari system standard yang ditawarkan oleh vendor dan penggunapun dapat merasakan tampilan bravia engine 2 yang lebih  tajam dansmooth. Kata Kunci :Replace System,Android
Prediksi Data Time-series menggunakan Jaringan Syaraf Tiruan Algoritma Backpropagation Pada Kasus Prediksi Permintaan Beras Gita Indah Marthasari; Silcillya Ayu Astiti; Yufis Azhar
Jurnal Informatika: Jurnal Pengembangan IT Vol 6, No 3 (2021): JPIT, September 2021
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v6i3.2627

Abstract

Recently, Indonesia, as a country where the majority of the population chooses rice as the primary food source, gets a decline in the rice consumption patterns, which resulted in reduced demand for rice that should have been stable. The decrease of rice purchasing power impacts several rice suppliers, commonly referred to as rice agents, to buy rice from rice production companies. Therefore, prediction of rice stock is essential to do. This paper aims to apply the backpropagation neural network method to forecast the amount of rice demand. The data used in the study is time-series data in the form of the number of requests for rice as much as 609 data from two types of rice. The modeling scenario in this study applies one to five hidden layers with a different number of hidden neurons in each experiment. The elastic net regularization method was applied after the data denormalization process to improve the quality of the resulting model. Based on the experiments, obtained the best model on architecture 7-50-200-300-250-300-1 with MSE = 0.001278, RMSE = 0.301950 in the training process and MSE results = 0.002391, RMSE = 0.204972 in the testing process.
Identifikasi Visual Cacat Produk Menggunakan Neural Network Model Backpropagation (Studi Kasus: PT. Panasonic Gobel Eco Solution) Muhammad Nur; Sjaeful Irwan; Danang Santosa
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.v4i2-2.1865

Abstract

Product defects are common in the production process. Visual identification of product defects is first carried out when the product is produced. Identification of vague defects in very small shapes with different sizes and positions is difficult to do with ordinary eye sight, so that often results in decisions about the status of the product that is not right. Product defects in visual form can be identified by patterns such as shape, size and position on the product image. In this study, we will apply a neural network with the backpropagation model as a classification of the pattern. Product images will be processed using image processing by converting the RGB pixel value of the image into a numeric value. Data in numerical form will be input for training values in the backpropagation model. Training results are used to identify identified product defects and produce product status decisions. The results show that the backpropagation neural network model is able to recognize product patterns with an accuracy of 99.24% and based on simulation test data with the final weight and bias of training results, able to identify product defects with success up to 91%.
Implementasi Algoritma Binary Tree dan Sequential Searching pada Aplikasi Web Multilevel Marketing Iyan Hadi Mulyana; Mohammad Rifqi
Jurnal Informatika: Jurnal Pengembangan IT Vol 5, No 3 (2020): JPIT, September 2020
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v5i3.2087

Abstract

Abstrak - Di Indonesia bisnis Multilevel Marketing (MLM) masih cukup banyak diminati masyarakat. Salah satu perusahaan yang bergerak dibidang bisnis Multilevel Marketing adalah PT. TSP dengan sistem Multilevel Marketing Binari. Sistem bisnis ini memvisualisasikan jaringan bisnisnya ke dalam pohon hirarki biner atau sering kita sebut Binary Tree. Pohon biner sendiri merupakan konsep pengorganisasian secara hirarki dari beberapa buah simpul dimana masing-masing simpul mempunyai maksimum 2 anak (child). Posisi simpul yang berada di atas simpul lainnya disebut induk (parent) dan simpul yang berada di bawah sebuah simpul disebut anak (child). Pembentukan pohon jaringan pada bisnis MLM sangatlah penting karena akan mempengaruhi hasil perhitungan bonus/komisi. Pada studi kasus PT. TSP terdapat permasalahan dalam menampilkan pohon jaringan bisnis MLM. Dimana terdapat beberapa simpul yang tidak mempunyai induk sehingga pohon jaringan terputus, tidak menjadi satu pohon jaringan yang utuh. Hal ini membuat perhitungan bonus/komisi menjadi sedikit kacau. Maka penerapan algoritma Binary Tree dirasa tepat untuk memvisualisasikan jaringan bisnis MLM. Algoritma Sequential Searching merupakan sebuah algoritma pencarian data secara urut yang prosesnya membandingkan setiap elemen satu per satu dari awal atau dari akhir.

Page 22 of 44 | Total Record : 431

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