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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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Sistem Pakar Diagnosis Penyakit Pada Ikan Bawal Bintang dengan Pendekatan Naive bayes Aldo, Dasril; Nur, Yohani Setiya Rafika; Fathoni, M. Yoka
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.v8i2.4750

Abstract

 The star pomfret is a type of cultivated fish that has high economic prospects. The focus of the main problem in this study is the disease that attacks the star pomfret fish commodity. If this is allowed to continue, it will cause crop failure and cause the fishermen to lose money. Through this research, an expert system is one solution that can overcome these problems. The expert system built will apply the Naive Bayes method with the stages of entering the dataset into the database which will be used as training data, then the user inputs testing data to be processed into the Bayes method, in the final result the probability value of each disease will be displayed which will then be given recommendations on how to control it disease. From the symptoms selected by the user, namely: white or pale spots on the surface of the body, bleeding on the surface of the body, protruding eyes, the fish looks difficult to breathe, mucus production increases until the body runs out of mucus / roughness, fish lose their appetite, slow movement and slow growth get disease results Cryptocaryon with a value of 93.4. The results of tests carried out on 17 data obtained an accuracy value of 94% so that the expert system is suitable for use as a tool for diagnosing disease in pomfret
Arsitektur Convolutional Neural Network (CNN) Alexnet Untuk Klasifikasi Hama Pada Citra Daun Tanaman Kopi Dicki Irfansyah; Metty Mustikasari; Amat Suroso
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.v6i2.2802

Abstract

Indonesia is the fourth largest coffee producing country in the world. However, when compared to 3 other countries, Indonesia's coffee production is still relatively small. Many factors cause this to happen, including the number of farmers' coffee trees that are attacked by diseases. If the handling of this disease is slow, then the disease in one tree can be transmitted to other trees. This causes a decrease in Indonesian coffee productivity. In this study, the author implemented the Alexnet Convolutional Neural Network (CNN) architecture using  the MATLAB programming platform for the identification of diseases in coffee plants through images. The total number of datasets used is 300 data which is divided into 3 classes, namely health, rust and red spider mite. The training process involving 260 training data resulted in an accuracy of 69.44-80.56%. The network testing process using 40 test data resulted in an accuracy of 81.6%. Based on the results of the study, it can be said that the Alexnet architecture is accurate for the classification of leaf pests on coffee plants
Association rules of Prognostic Variables for Survival in a Randomized Comparison of Treatments for Prostatic Cancer Iing Lukman; Emy Khikmawati
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.v4i1.1252

Abstract

In this paper, the data was analyzed by data mining techniques of association rules. The data for 506 patients consist of an identification number, stage of tumour, a code for the treatment to which the patient was assigned, the date of randomization, the total months of follow-up since randomization, an indicator for the survival status or cause of death, and the values of twelve pretreatment covariates. The goal of an analysis should be to compare the treatments with respect to survival of the patients. Since this was a randomized study it would ordinarily not be necessary to adjust for the values of the pretreatment covariates. However, in such studies it is advisable to examine the prognostic significance of the covariates and to confirm that they are balanced across treatment groups.  In addition, the analyst should look for important treatment-covariates interactions which might lead to the definition of subsets of patients in which treatment differences were significantly more marked or even reversed.
Klasifikasi Penyakit Daun Padi Berdasarkan Hasil Ekstraksi Fitur GLCM Interval 4 Sudut Jani Kusanti; Noor Abdul Haris
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.v3i1.669

Abstract

One of the factors causing rice production depression is a typical disease in rice plants. Typical of disease in rice plants, among others, such Blast Disease, Leaf Blight Disease, Disease Hawar On Stem, Crackle Disease and so on. Each type of disease requires different treatment, but not all farmers know the type of disease so as to allow for errors in the handling. This research made an application program that can identify rice pests to facilitate farmers solve the problems of rice plants disease since it becomes important to make a disease classification system on the leaves of rice plants. This research uses backpropagation method to classify the type of disease resulting from feature extraction of GLCM with 4 angles. Results obtained 80% accuracy from 30 data, with 16 seconds testing time.  
Analisis Sentimen Berbasis Aspek pada Layanan Hotel di Wilayah Kabupaten Banyumas dengan Word2Vec dan Random Forest Wijayanto, Sena; Prabowo, Dedy Agung; Kristiyanto, Daniel Yeri; Fathoni, M Yoka
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.v8i1.4186

Abstract

Dalam industri pariwisata, hotel memiliki peran penting untuk membantu wisatawan karena menyediakan penginapan terutama bagi wisatawan dari luar kota. Kualitas layanan hotel dapat dilihat dari opini-opini yang diberikan ooleh pengunjung yang telah menginap di hotel tersebut. Penelitian ini bertujuan untuk melakukan analisis sentimen terhadap ulasan yang diberikan oleh pengunjung hotel. Data ulasan tersebut diambil dari Traveloka menggunakan web scrapping. Metode yang digunakan untuk ekstraksi fitur adalah word2vec. Untuk klasifikasi sentimen, metode yang digunakan adalah random forest. Hasil percobaan terbaik didapatkan dari hasil percobaan dengan menggunakan jumlah tree 100, 200, dan 300 dengan hasil akurasi sebesar 82%-83%.
Deteksi Penyakit Tanaman Cabai Menggunakan Algoritma YOLOv5 Dengan Variasi Pembagian Data Riva, Laurenza Setiana; Jayanta, Jayanta
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.v8i3.5679

Abstract

Rapid technological developments have resulted in various innovative techniques that help humans, including object detection which functions to identify each element in an image. Object detection is often used to overcome problems that occur because of its ability to identify each element in the image. One of the problems that is often encountered is a decrease in agricultural income due to disease in chili plants. The maintenance of chili plants has various obstacles including the impact of weather which causes the development of diseases and pests so that chili production has decreased. By implementing the object detection, farmers can easily identify diseases that attack chili plants through pictures so that chili disease can be treated more quickly. This study uses the YOLOv5 algorithm to test the performance of the model in identifying diseases in chili plants. Pictures were taken using a cellphone camera with dimensions of 3472x3472 pixels. The amount of image data used is 430 data. Image data is divided into 3 parts, namely train data, validation data, and test data. To get the best model, this study also conducted three experiments with different distribution of data. Experiment 1 with a division of 70:20:10, experiment 2 with a division of 75:15:10, and experiment 3 with a division of 80:10:10. From the experiments carried out, the best results were obtained, namely in experiment 3 with an average value obtained in the test of 0.947 with a translation of the precision, recall, and mAP values, namely 0.946, 0.936, and 0.959 respectively.
PENGGUNAAN DAN PENERAPAN TEKNOLOGI INFORMASI SEBAGAI SISTEM PENDUKUNG KEPUTUSAN (SPK) (Studi Kasus di Lembaga Pemerintahan Daerah Kabupaten Jepara) Suyatno Suyatno
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.v1i2.396

Abstract

Dengan penuh semangat, gevernments provinsi bersaing untuk menerapkan teknologi informasi dalam mereka dengan provinsi. Namun, mempertanyakan ide tentang kelayakan dan manfaat dari program dengan kenaikan implantasi kutu. Sistem pendukung ini pengambilan keputusan akan menganalisis biaya dan keuntungan dari teknologi informasi di mikro dengan menggunakan metode informasi Ekonomi (IE), Marilyn 88. Metode ini memperluas sistem akuntansi yang bernama ROI (Return On Investment) dengan menambahkan dua domain lain yang; bisnis dan teknologi. Dengan hasil yang kita dapat membuat kesimpulan apakah layanan teknologi informasi adil atau tidak dengan menggabungkan bisnis dan teknologi dan perusahaan factros.Katakunci : Pemakai, TI, SPK
Analisis K-Nearest Neibord Berbasis Forward Selection dalam Prediksi Status Mahasiswa Non Aktif pada STMIK Bani Saleh Panca Indah Lestari
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.v6i3.2794

Abstract

Masalah yang dihadapi dalam pengelolaan data aktivitas kuliah mahasiswa (AKM) salah satunya dalam menentukan total Satuan Kredit Semester (SKS) dan Indeks Prestasi Kumulatif (IPK) pada mahasiswa non aktif. Dalam melakukan pengelolaan data akademik menjadi informasi sebagai aspek pengambilan keputusan dalam menentukan keaktifan mahasiswa. Beberapa faktor seperti Indek Prestasi Semester (IPS), Jumlah SKS Semester, Indek Prestasi Kumulatif (IPK), Jumlah SKS Total, Biaya dan Status Mahasiswa. Langkah untuk mencegah indikasi mahasiswa non aktif perlu dilakukan analisis pola prediksi untuk menentukan sisa masa studi mahasiswa serta menghasilkan informasi yang akurat dan sebagai bahan prediksi untuk membandingkan data pertahun akademik terhadap mahasiswa non aktif K-NN berbasis Forward Selection. Penelitian prediksi mahasiswa non aktif menggunakan pengujian menggunakan menggunkan Rapid Miner terhadap dataset mahasiswa sebanyak 342, menghasilkan nilai akurasi K-Nearest Neighbor (k-3) sebesar 93,55% dan Forward Selection (k-3) sebesar 99,39%. dari hasil analisis didapatkan data mahasiswa yang akan Drop Out sebesar 1160 sebagai usulan untuk manajemen pada periode pelaporan berikutnya. maka penelitian dapat dikembangkan lebih lanjut untuk penentuan nilai k yang lebih optimal dengan menambahkan aspek klasifikasi status mahasiswa bekerja atau tidak bekerja.
E-Learning Satisfaction Menggunakan Metode Auto Model Arif Rinaldi Dikananda; Fidya Arie Pratama; Ade Rizki Rinaldi
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.v4i2-2.1864

Abstract

E-Learning just like learning media in general need to be evaluated to find out and measure how much effectiveness, efficiency and user satisfaction with the quality of the overall learning process. One effort that can be done to find out and evaluate the quality of a learning is to use satisfaction evaluation. Measurement of satisfaction requires data derived from questionnaires that are presented using a Likert scale. The data illustrates the perception of users who have uncertainty because it is very subjective so that it has the potential to cause misinterpretation. The auto model method can be used to evaluate e-Learning satisfaction because the auto model method has the advantage of solving a problem with the various models produced, which in this case are in accordance with the context of the satisfaction problem that is often presented in natural language that has uncertainty, such as "how satisfied? "," How efficient? "And" how much is user satisfaction. Based on the auto model method, the results of the satisfaction scores of each respondent, shown in the table above, are summed and the average is calculated. With the auto model, the results show that SVM is the best performance method with an acceleration rate of 90% and best gains with a value of 38.
Prediksi Kemunculan Titik Panas Di Lahan Gambut Provinsi Riau Menggunakan Long Short Term Memory Ulfa Khaira; Muksin Alfalah; Pikir Claudia Septiani Gulo; Robi Purnomo
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.v5i3.1931

Abstract

 Indonesia is blessed with the largest and most diverse tropical forests in the world. Millions of Indonesians depend on these forests for their lives. But lately forest fires have become an international concern as an environmental and economic issue. One of the causes of the decline in the number of forests is forest fires. Forest fires produce high particle emissions which can endanger human health. For this reason, necessary precautions. One prevention that can be done is to predict the emergence of hotspots, especially in areas where forest fires are frequent. One way to reduce forest fires is to predict the emergence of hot spots on peatlands with the Long Short Term Memory (LSTM) method. This study predicts the emergence of hotspots in Riau Province over the next 6 months, from August 2019 to January 2020. LSTM is able to predict time series with RMSE 363.38.

Page 16 of 44 | Total Record : 431

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