Jurnal Komputasi最新文献_第4页

Aplikasi DonasiKu Berbasis Android 我的捐赠应用是基于Android的

Jurnal Komputasi Pub Date : 2022-04-30 DOI: 10.23960/komputasi.v10i1.2933

Muhamad Bahrul Ulum, Habibullah Akbar, Anik Hanifatul Azizah

{"title":"Aplikasi DonasiKu Berbasis Android","authors":"Muhamad Bahrul Ulum, Habibullah Akbar, Anik Hanifatul Azizah","doi":"10.23960/komputasi.v10i1.2933","DOIUrl":"https://doi.org/10.23960/komputasi.v10i1.2933","url":null,"abstract":"Used goods suitable for use are old goods that have been used once or more than once and are still reasonable to be reused. Most of these used goods are no longer used because they already have other, better substitutes. The current management system for these items is usually collected and stored in the warehouse or many scattered in the corner house until the items become a pile. Rather than being left alone, it is better to reuse these items. For example, they are donating items that are still reasonable to be reused to people who need them more. Donations in the form of used goods are still poorly managed, information on donation activities that are held is less spread out, and if there is, it is not necessarily reliable. So far, the management of the existing donation system is only for donations in the form of money, so the idea of a solution emerged in developing an Android-based donation system application. Causal analysis is used to analyze the problem based on the data to be collected. The method used for software development is extreme programming. This application can solve the problem of managing the used goods donation system so that people can make donations in the form of goods that are faster easier to collect and distribute.","PeriodicalId":292117,"journal":{"name":"Jurnal Komputasi","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132929826","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

ANALISIS DAN PERANCANGAN KNOWLEDGE MANAGEMENT SYSTEM UNTUK MENINGKATKAN KINERJA PEGAWAI PADA BADAN NARKOTIKA NASIONAL KOTA TANGERANG SELATAN BERBASIS WEBSITE

Jurnal Komputasi Pub Date : 2022-04-30 DOI: 10.23960/komputasi.v10i1.2950

Rosalina Aprianti, Y. Sugiarti

引用次数: 0

Perbandingan Nilai K pada Klasifikasi Pneumonia Anak Balita Menggunakan K-Nearest Neighbor

Jurnal Komputasi Pub Date : 2022-04-30 DOI: 10.23960/komputasi.v10i1.2965

Dwi Kartini, Andi Farmadi, M. Muliadi, Dodon Turianto Nugrahadi, Pirjatullah Pirjatullah

{"title":"Perbandingan Nilai K pada Klasifikasi Pneumonia Anak Balita Menggunakan K-Nearest Neighbor","authors":"Dwi Kartini, Andi Farmadi, M. Muliadi, Dodon Turianto Nugrahadi, Pirjatullah Pirjatullah","doi":"10.23960/komputasi.v10i1.2965","DOIUrl":"https://doi.org/10.23960/komputasi.v10i1.2965","url":null,"abstract":"Pneumonia is an infectious disease that attacks the lower respiratory tract and is one of the main causes of death in children under five. Pneumonia is easy to attack toddlers caused by various microorganisms that exist in the environment such as viruses, bacteria, fungi and micro bacteria. This study uses K-Nearest Neighbor (KNN) for the classification of pneumonia in patients based on the symptoms experienced. The KNN classification method is carried out by comparing the object distance between the test data and the overall object on the training data based on the patient's medical history data. The comparison of the percentage of the training data and the test data used is 90:10, 80:20, and 70:30 to calculate the value of the closest distance of the test data to the overall training data with the number of k used. The confusion matrix was used to measure the results of the Pneumonia classification test for toddlers with a combination of the amount of training data and test data on the number of k={1, 3, 5, 7, 9, 11}, the highest accuracy, precision, recall, and F-measure values were obtained. 0.86, 0.89, 1, and 0.91 for 90% training data, 10% test data with a value of k = 3. Keywords: Pneumonia, Toddlers, KNN, Confusion Matrix.","PeriodicalId":292117,"journal":{"name":"Jurnal Komputasi","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129848820","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Implementasi Metode Naive Bayes pada Sistem Pakar Diagnosis Penyakit Kutu Ikan Gurami (Argunus Indicus) 将“刺痛鱼”(Argunus Indicus)的治疗方法应用于“刺痛鱼”系统中的天真贝斯(Naive Bayes)

Jurnal Komputasi Pub Date : 2022-04-30 DOI: 10.23960/komputasi.v10i1.2956

A. Wantoro, Heni Sulistiyani, Yodhi Yuniarthe, Arie Setya Putra, Apri Candra Widyawati, Nanda Putra Wicaksono

{"title":"Implementasi Metode Naive Bayes pada Sistem Pakar Diagnosis Penyakit Kutu Ikan Gurami (Argunus Indicus)","authors":"A. Wantoro, Heni Sulistiyani, Yodhi Yuniarthe, Arie Setya Putra, Apri Candra Widyawati, Nanda Putra Wicaksono","doi":"10.23960/komputasi.v10i1.2956","DOIUrl":"https://doi.org/10.23960/komputasi.v10i1.2956","url":null,"abstract":"Gouramy is a freshwater fish that is widely cultivated by breeders and many experience death. Many deaths are caused by various diseases such as fungi and fish lice. The presence of disease in gouramy causes losses due to the number of deaths and can reduce quality such as freshness, color, and body defects which can affect the selling price of fish or economic value. Gouramy mortality data can reach up to 50%-100%. To reduce losses due to high mortality, an expert is needed to diagnose the disease. But the fact is that not all gouramy farmers understand how to diagnose fish, therefore an expert system is needed that can be used to help farmers to diagnose fish lice disease based on symptoms. The results of the system evaluation using 20 (twenty) fish symptom data obtained from carp breeders in 2021 which were compared with expert beliefs calculated using the confusion matrix table, the accuracy values were 94.2%, precision 95%, sensitivity 95% and specivity 93.3%. The evaluation results prove that Naïve Bayes has succeeded in providing good diagnostic results, so that the developed system can be used by fish farmers in diagnosing gouramy disease. Keywords: Gourami; Diagnosis; Fish Fleas; Naive Bayes; Expert system","PeriodicalId":292117,"journal":{"name":"Jurnal Komputasi","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130341776","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Peningkatan Kemenangan Non-Playable Character dalam Permainan Triple Triad Menggunakan Alpha-Beta Pruning Peningkatan Kemenangan不可玩角色dalam Permainan Triple Triad Menggunakan Alpha-Beta Pruning

Jurnal Komputasi Pub Date : 2022-04-30 DOI: 10.23960/komputasi.v10i1.2952

Benedictta Dinda Permatasari, Hanny Haryanto, Erna Zuni Astuti, Erlin Dolphina

{"title":"Peningkatan Kemenangan Non-Playable Character dalam Permainan Triple Triad Menggunakan Alpha-Beta Pruning","authors":"Benedictta Dinda Permatasari, Hanny Haryanto, Erna Zuni Astuti, Erlin Dolphina","doi":"10.23960/komputasi.v10i1.2952","DOIUrl":"https://doi.org/10.23960/komputasi.v10i1.2952","url":null,"abstract":"Non-Playable Character (NPC) is one of the essential elements in video games. Generally, NPCs provide challenges for players in completing missions in the game, where NPCs mean acting as enemies. The role of the enemy causes the victory rate to be one of the main goals of artificial intelligence applied to NPCs. The challenges that these NPCs provide are significant to keep players going. NPCs must be able to provide a balanced challenge like humans to have an experience that is as enjoyable as when playing with other people. The problem is the low win rate achieved by NPCs so that players can feel bored. The alpha-beta pruning algorithm is one of the decision-making algorithms that is often applied to games that require more than or equal two players. Therefore, this algorithm is suitable for applying to the object of research, namely the Triple Triad game. The Triple Triad game is a board game played by two players. The Triple Triad game was first introduced as a mini-game in the Final Fantasy VIII game. This game is a combination of card games and board games. In this study, the alpha-beta pruning algorithm was proven to increase the win rate of NPCs. It is indicated by comparing the win rate of NPCs who choose a random step, which is 17.5%, with an NPC that has applied the alpha-beta pruning algorithm, which is 55%. Therefore, there is a significant increase in the win rate. Keywords: Alpha-beta pruning; artificial intelligence; card; game; Non-Playable Character.","PeriodicalId":292117,"journal":{"name":"Jurnal Komputasi","volume":"76 8","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120922950","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

IMPLEMENTASI SUPPORT VECTOR MACHINE (SVM) UNTUK KLASIFIKASI PEDERITA DIABETES MELLITUS MELLITUS糖尿病患者分类的支持机(SVM)的执行

Jurnal Komputasi Pub Date : 2022-04-30 DOI: 10.23960/komputasi.v10i1.2940

Favorisen R. Lumbanraja, Fanni Lufiana, Yunda Heningtyas, Kurnia Muludi

{"title":"IMPLEMENTASI SUPPORT VECTOR MACHINE (SVM) UNTUK KLASIFIKASI PEDERITA DIABETES MELLITUS","authors":"Favorisen R. Lumbanraja, Fanni Lufiana, Yunda Heningtyas, Kurnia Muludi","doi":"10.23960/komputasi.v10i1.2940","DOIUrl":"https://doi.org/10.23960/komputasi.v10i1.2940","url":null,"abstract":"Diabetes Mellitus (DM) is a chronic disease characterized by the body's inability to metabolize carbohydrates, fats, and proteins, resulting in increased blood sugar (hyperglycemia) due to low insulin levels. Diabetes is due to a combination of heredity (genetics) and unhealthy lifestyles. Hemoglobin A1c is a blood test used to diagnose and manage diabetes patients when measuring blood sugar levels. This study aims to analyze predictive models for the classification of people with diabetes using R Shiny and evaluate the results of the support vector machine method's classification performance. There are many ways to diagnose diabetes, and the support vector machine is one of the machine learning algorithms used in this study's classification case (SVM). This study uses data from Diabetes 130-US Hospital For Years 1999-2008, which was sourced from the UCI Machine Learning Repository and consists of 34 variables and 84900 records, with dataset distribution and testing techniques using the 10-fold cross-validation method and three kernels in modeling using SVM, namely linear, Gaussian, and polynomial. The results obtained are a simple predictive model analysis system for classifying people with diabetes with shiny, making it easier for users to find out the prediction results and obtain the highest accuracy result, which is 82.76 percent of the gaussian kernel. Keywords: diabetes mellitus; HbA1c; classification; support vector machine; 10-fold cross validation.","PeriodicalId":292117,"journal":{"name":"Jurnal Komputasi","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128982761","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Klasifikasi Image Tumbuhan Obat (Keji Beling) Menggunakan Artificial Neural Network 利用人工神经网络对药用植物（枸杞）进行图像分类

Jurnal Komputasi Pub Date : 2021-10-30 DOI: 10.23960/komputasi.v9i2.2868

Rizky Prabowo, Yunda Heningtyas, Machudor Yusman, Muhammad Iqbal, Ossy Dwi Endah Wulansari

{"title":"Klasifikasi Image Tumbuhan Obat (Keji Beling) Menggunakan Artificial Neural Network","authors":"Rizky Prabowo, Yunda Heningtyas, Machudor Yusman, Muhammad Iqbal, Ossy Dwi Endah Wulansari","doi":"10.23960/komputasi.v9i2.2868","DOIUrl":"https://doi.org/10.23960/komputasi.v9i2.2868","url":null,"abstract":"ndonesia sebagai salah satu negara tropis memiliki potensi hayati yang sangat besar. Salah satu potensi yang banyak dimiliki di Indonesia adalah tumbuhan obat. Salah satu cara mengenali jenis tumbuhan obat yaitu melalui bentuk fisik daun. Implementasi teknologi yang saat ini banyak berkembang, maka masyarakat akan banyak terbantu dalam mengenali tumbuhan obat disekitarnya. Gambar atau citra daun tanaman obat digunakan sebagai data yang mewakili jenis tumbuhan obat tertentu. Data yang digunakan merupakan data yang telah diberikan perlakuan khusus dalam pengambilan gambar atau citra. Praprosesing dilakukan pada data yang didapat sebagai langkah awal pemrosesan data. Pada penelitian ini, data yang digunakan merupakan data primer dengan total 2000 data. Data yang digunakan dibagi menjadi 1800 data latih, 160 data validasi dan 200 data testing. Data training digunakan untuk membentuk pola model. Model selanjutnya di validasi dengan menggunakan data validasi. Model dibangun menggunakan Convulution Neural Network yang merupakan varian dari Artificial Neural Network. Hasil akurasi penelitian 82.5% dengan kecepatan pembangunan model dengan 10 epoch adalah 139 second per epoch.","PeriodicalId":292117,"journal":{"name":"Jurnal Komputasi","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134034680","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

STUDI MOBILE COMMERCE DI BANDAR LAMPUNG

Jurnal Komputasi Pub Date : 2021-04-30 DOI: 10.23960/KOMPUTASI.V9I1.2741

Tristiyanto Tristiyanto, Syintia Dwi Nurrahmi, Yunda Heningtyas

引用次数: 0

SISTEM PENDETEKSI PENULISAN KATA PADA DOKUMEN BERBASIS WEB 基于WEB的文档的文字检测系统

Jurnal Komputasi Pub Date : 2021-04-30 DOI: 10.23960/KOMPUTASI.V9I1.2793

A. Saputra, Dwi Sakethi, Ytu Yohana Tri Utami

引用次数: 0

SISTEM IDENTIFKASI PENYAKIT TANAMAN CABAI MENGGUNAKAN METODE SIMPLE ADDITIVE WEIGHTING (SAW) BERBASIS ANDROID 辣椒植物疾病的识别系统采用了一种简单的上瘾方法以ANDROID为基础

Jurnal Komputasi Pub Date : 2021-04-30 DOI: 10.23960/KOMPUTASI.V9I1.2421

B. HendireS, Machudor Yusman

引用次数: 0