Articles
<![CDATA[Penerapan Metode Naive Bayes pada Sistem Klasifikasi Kualitas Biji Kopi Robusta ]]>
<![CDATA[Muhammad Irsyad Indra Fata]]>;
<![CDATA[Donny Avianto]]>
<![CDATA[ Jurnal Indonesia : Manajemen Informatika dan Komunikasi Vol. 5 No. 1 (2024): Jurnal Indonesia : Manajemen Informatika dan Komunikasi (JIMIK) ]]>
Publisher : <![CDATA[Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) AMIK Indonesia ]]>
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DOI: 10.35870/jimik.v5i1.515
<![CDATA[In Indonesia, coffee has become a necessity for some people because the effects can increase stamina and mood so that it can make a person's activities more productive, therefore quality coffee beans are needed so that coffee drinks can produce maximum effects and can be enjoyed by the public This is the basis for researchers to conduct research on the classification of superior quality coffee beans with accurate accuracy. Researchers use the naïve Bayes classifier method because this algorithm is considered more effective in cases of object classification compared to classification carried out manually. Researchers used 220 primary data with parameter configurations, namely, test size 0.2, random state 15, and Gausian model. The accuracy results obtained after testing were 0.91 for training data and 0.86 for testing data. It was concluded that the Naive Bayes method proved to be quite effective for classifying the coffee beans studied]]>
<![CDATA[Implementasi Algoritma LSTM untuk Prediksi Harga Cabai Merah Keriting di Yogyakarta ]]>
<![CDATA[Arieska Restu Harpian Dwika]]>;
<![CDATA[Donny Avianto]]>
<![CDATA[ Jurnal Indonesia : Manajemen Informatika dan Komunikasi Vol. 5 No. 1 (2024): Jurnal Indonesia : Manajemen Informatika dan Komunikasi (JIMIK) ]]>
Publisher : <![CDATA[Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) AMIK Indonesia ]]>
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DOI: 10.35870/jimik.v5i1.534
<![CDATA[Curly red chili is one of the horticultural crops with high economic value. Curly red chili can cause inflation because the price fluctuates greatly and tends to rise. The purpose of the research is to predict future prices quickly and accurately so that the government and consumers can take preventive action against existing problems. This research will use the LSTM algorithm and a dataset of curly red chili prices in Yogyakarta from October 9, 2017 to October 27, 2023. Based on the testing of this research, the best results obtained are by using a data division ratio of 70%: 30%, epoch 150, batch size 48, learning rate 0.001, number of neurons 30, activation function ReLU, and the optimization function Adam which produces a MAPE value of 3.6995%, and an accuracy of 96.3005%. It is hoped that this system can help related parties get accurate curly red chili price predictions.]]>
<![CDATA[Perancangan Aplikasi Layanan Pengaduan Kerusakan Jalan Berbasis Android ]]>
<![CDATA[Rizky Samudra Falasyfa]]>;
<![CDATA[Donny Avianto]]>
<![CDATA[ Jurnal Indonesia : Manajemen Informatika dan Komunikasi Vol. 5 No. 1 (2024): Jurnal Indonesia : Manajemen Informatika dan Komunikasi (JIMIK) ]]>
Publisher : <![CDATA[Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) AMIK Indonesia ]]>
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DOI: 10.35870/jimik.v5i1.569
<![CDATA[The android-based road damage complaint service application is a technological innovation that aims to improve the reporting and management of road damage. In the era of digitalization, people expect a lot from public services, especially infrastructure. However, the reporting process is still often done manually, causing weaknesses such as data loss, slow response and ineffectiveness. This application is an effective solution to overcome these problems. Users can easily report road damage through their Android devices. The benefit of this application is the ability to know the level of road damage in each report, making it easier for the authorities to determine the priority of handling. In addition, the app allows users to provide a detailed description of the damage, upload photos as evidence, and add additional relevant information. The app provides a complete and easy-to-fill form, making the reporting process more efficient.Once the report is submitted, users will receive a confirmation of report receipt to track the status of the report. The relevant agencies will utilize the app as a tool to manage incoming complaints, promptly responding to reports and processing them based on the priority and type of damage reported. Therefore, this application brings significant changes by improving public services and ensuring better infrastructure maintenance.]]>
<![CDATA[Implementasi Web Klasifikasi Suasana Hati Berdasarkan Potongan Lagu dengan Memanfaatkan Convolutional Neural Network ]]>
<![CDATA[Roy Fasti]]>;
<![CDATA[Donny Avianto]]>
<![CDATA[ Jurnal Indonesia : Manajemen Informatika dan Komunikasi Vol. 5 No. 1 (2024): Jurnal Indonesia : Manajemen Informatika dan Komunikasi (JIMIK) ]]>
Publisher : <![CDATA[Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) AMIK Indonesia ]]>
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DOI: 10.35870/jimik.v5i1.573
<![CDATA[Music is often used to accompany the user according to his heart condition. So users often create a playlist by adjusting the mood they are feeling. However, there are some users who have had difficulties in making playlists because in making a playlist it has to be done manually, that is, listening to music one at a time, wasting a lot of time. Therefore, the author conducted research on the classification of the mood contained in music and created a system that works to help classify music automatically by using one method that is part of deep learning, the method mentioned by the author is the Convolutional Neural Network (CNN) method. As for the data used by the investigator in this study is music data with a lot of data amounting to 400 data, on such data is done preprocessing data by cutting the duration of music and converting music into image. The next step is to split the data, dividing it into training data and test data. The training data is divided by 80% and the test data is also split by 20% of the total datasets used by the author. The results of this data division were used to build a model using the CNN model. The accuracy results obtained in this study were 95% for the training accurately and 68% for the data validation accurate.]]>
<![CDATA[Sistem Pakar Diagnosa Kelainan Stunting Balita Menggunakan Metode KNN Berbasis Web ]]>
<![CDATA[Amalia Rizki Wulandari]]>;
<![CDATA[Donny Avianto]]>
<![CDATA[ Jurnal Indonesia : Manajemen Informatika dan Komunikasi Vol. 5 No. 1 (2024): Jurnal Indonesia : Manajemen Informatika dan Komunikasi (JIMIK) ]]>
Publisher : <![CDATA[Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) AMIK Indonesia ]]>
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DOI: 10.35870/jimik.v5i1.587
<![CDATA[Stunting in young children is a child nutrition problem in Indonesia. This research has the main objective of preventing and overcome it through a technique that can predict stunted babies based on data or information. This study using KNN method uses 125 test data and the value of k = 3 gives an accuracy of 96%. This includes 119 accurate predictions and 6 inaccurate predictions. The aim of this study is to predict the outcome of stunting in toddlers. If they fall into the stunting category, the parents of the toddler must pay more attention to the nutritional development of the toddler to be able to minimize the stunting rate in Indonesia.]]>
<![CDATA[Max Depth Impact on Heart Disease Classification: Decision Tree and Random Forest ]]>
<![CDATA[Rian Oktafiani]]>;
<![CDATA[Arief Hermawan]]>;
<![CDATA[Donny Avianto]]>
<![CDATA[ Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 1 (2024): February 2024 ]]>
Publisher : <![CDATA[Ikatan Ahli Informatika Indonesia (IAII) ]]>
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DOI: 10.29207/resti.v8i1.5574
<![CDATA[Results in heart disease classification that are inaccurate and have low accuracy can endanger the patient's life. Some parameters in the algorithm model also influence classification. This study compares the Decision Tree and Random Forest algorithms for heart disease. The influence of maximum depth on heart disease classification also has significant implications. If the maximum depth is not set correctly, the classification results can be inaccurate and lead to incorrect diagnoses. This study uses five data split schemes, namely 60%: 40%, 70%: 30%, 75%: 25%, 80%: 20%, 90%: 10% and tested with different max depth parameters, namely max depth = 3, 4, 5, 6, and 7. This research produces the best accuracy using the 90%:10% scheme and max depth = 7 with the best accuracy result using the Random Forest algorithm of 99.29% while the Decision Tree algorithm is 98.05%. Then the precision and recall value of the Random Forest algorithm is 99% while the Decision Tree is 98%. The results of computation time using Decision Tree are faster than using Random Forest with a computation time for training data of 0.0075 s, while the testing data are 0.009 s. In future research, research can be conducted on the effect of other parameters by testing using several data sets.]]>
<![CDATA[Penerapan Metode Naive Bayes pada Sistem Klasifikasi Kualitas Biji Kopi Robusta ]]>
<![CDATA[Muhammad Irsyad Indra Fata]]>;
<![CDATA[Donny Avianto]]>
<![CDATA[ Jurnal Indonesia : Manajemen Informatika dan Komunikasi Vol. 5 No. 1 (2024): Jurnal Indonesia : Manajemen Informatika dan Komunikasi (JIMIK) ]]>
Publisher : <![CDATA[Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Indonesia Banda Aceh ]]>
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DOI: 10.35870/jimik.v5i1.515
<![CDATA[In Indonesia, coffee has become a necessity for some people because the effects can increase stamina and mood so that it can make a person's activities more productive, therefore quality coffee beans are needed so that coffee drinks can produce maximum effects and can be enjoyed by the public This is the basis for researchers to conduct research on the classification of superior quality coffee beans with accurate accuracy. Researchers use the naïve Bayes classifier method because this algorithm is considered more effective in cases of object classification compared to classification carried out manually. Researchers used 220 primary data with parameter configurations, namely, test size 0.2, random state 15, and Gausian model. The accuracy results obtained after testing were 0.91 for training data and 0.86 for testing data. It was concluded that the Naive Bayes method proved to be quite effective for classifying the coffee beans studied]]>
<![CDATA[Implementasi Algoritma LSTM untuk Prediksi Harga Cabai Merah Keriting di Yogyakarta ]]>
<![CDATA[Arieska Restu Harpian Dwika]]>;
<![CDATA[Donny Avianto]]>
<![CDATA[ Jurnal Indonesia : Manajemen Informatika dan Komunikasi Vol. 5 No. 1 (2024): Jurnal Indonesia : Manajemen Informatika dan Komunikasi (JIMIK) ]]>
Publisher : <![CDATA[Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Indonesia Banda Aceh ]]>
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Download Original
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Check in Google Scholar
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DOI: 10.35870/jimik.v5i1.534
<![CDATA[Curly red chili is one of the horticultural crops with high economic value. Curly red chili can cause inflation because the price fluctuates greatly and tends to rise. The purpose of the research is to predict future prices quickly and accurately so that the government and consumers can take preventive action against existing problems. This research will use the LSTM algorithm and a dataset of curly red chili prices in Yogyakarta from October 9, 2017 to October 27, 2023. Based on the testing of this research, the best results obtained are by using a data division ratio of 70%: 30%, epoch 150, batch size 48, learning rate 0.001, number of neurons 30, activation function ReLU, and the optimization function Adam which produces a MAPE value of 3.6995%, and an accuracy of 96.3005%. It is hoped that this system can help related parties get accurate curly red chili price predictions.]]>
<![CDATA[Perancangan Aplikasi Layanan Pengaduan Kerusakan Jalan Berbasis Android ]]>
<![CDATA[Rizky Samudra Falasyfa]]>;
<![CDATA[Donny Avianto]]>
<![CDATA[ Jurnal Indonesia : Manajemen Informatika dan Komunikasi Vol. 5 No. 1 (2024): Jurnal Indonesia : Manajemen Informatika dan Komunikasi (JIMIK) ]]>
Publisher : <![CDATA[Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Indonesia Banda Aceh ]]>
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DOI: 10.35870/jimik.v5i1.569
<![CDATA[The android-based road damage complaint service application is a technological innovation that aims to improve the reporting and management of road damage. In the era of digitalization, people expect a lot from public services, especially infrastructure. However, the reporting process is still often done manually, causing weaknesses such as data loss, slow response and ineffectiveness. This application is an effective solution to overcome these problems. Users can easily report road damage through their Android devices. The benefit of this application is the ability to know the level of road damage in each report, making it easier for the authorities to determine the priority of handling. In addition, the app allows users to provide a detailed description of the damage, upload photos as evidence, and add additional relevant information. The app provides a complete and easy-to-fill form, making the reporting process more efficient.Once the report is submitted, users will receive a confirmation of report receipt to track the status of the report. The relevant agencies will utilize the app as a tool to manage incoming complaints, promptly responding to reports and processing them based on the priority and type of damage reported. Therefore, this application brings significant changes by improving public services and ensuring better infrastructure maintenance.]]>
<![CDATA[Implementasi Web Klasifikasi Suasana Hati Berdasarkan Potongan Lagu dengan Memanfaatkan Convolutional Neural Network ]]>
<![CDATA[Roy Fasti]]>;
<![CDATA[Donny Avianto]]>
<![CDATA[ Jurnal Indonesia : Manajemen Informatika dan Komunikasi Vol. 5 No. 1 (2024): Jurnal Indonesia : Manajemen Informatika dan Komunikasi (JIMIK) ]]>
Publisher : <![CDATA[Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Indonesia Banda Aceh ]]>
Show Abstract
|
Download Original
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Original Source
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Check in Google Scholar
|
DOI: 10.35870/jimik.v5i1.573
<![CDATA[Music is often used to accompany the user according to his heart condition. So users often create a playlist by adjusting the mood they are feeling. However, there are some users who have had difficulties in making playlists because in making a playlist it has to be done manually, that is, listening to music one at a time, wasting a lot of time. Therefore, the author conducted research on the classification of the mood contained in music and created a system that works to help classify music automatically by using one method that is part of deep learning, the method mentioned by the author is the Convolutional Neural Network (CNN) method. As for the data used by the investigator in this study is music data with a lot of data amounting to 400 data, on such data is done preprocessing data by cutting the duration of music and converting music into image. The next step is to split the data, dividing it into training data and test data. The training data is divided by 80% and the test data is also split by 20% of the total datasets used by the author. The results of this data division were used to build a model using the CNN model. The accuracy results obtained in this study were 95% for the training accurately and 68% for the data validation accurate.]]>
