Articles
<![CDATA[Prediksi Penjualan Pertalite Menggunakan Metode Support Vector Regression ]]>
<![CDATA[Rachmawan Sidik Laminullah]]>;
<![CDATA[Haditsah Annur]]>;
<![CDATA[Irma Surya Kumala I]]>
<![CDATA[ Jurnal Cosphi Vol 4, No 1 (2020): Januari-Juli 2020 ]]>
Publisher : <![CDATA[Teknik Elektro - Universitas Ichsan Gorontalo ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
Full PDF (476.786 KB)
<![CDATA[Bahan Bakar Minyak atau BBM adalah salah satu komoditas sumber daya alam minyak dan gas bumi. Minyak dan gas bumi juga merupakan sumber daya alam yang tak terbarukan dan dikuasai oleh Negara. Kehidupan mayarakat Indonesia saat ini sangat bergantung pada BBM karena pengguna kendaraan bermotor yang semakin lama semakin bertambah banyak sehingga permintaan akan BBM pun makin melonjak. Maka dari itu, sistem prediksi sangat dibutuhkan untuk memprediksi penjualan guna meminimalisir resiko yang ditimbulkan akibat overload stok dan sangat diharapkan untuk dapat meningkatkan omset penjualan di SPBU tersebut. Metode Support Vector Regression adalah salah satu metode yang sering digunakan untuk prediksi data karena menghasilkan hasil akurasi prediksi yang tinggi, namun belum pernah digunakan untuk memprediksi hasil penjualan Pertalite. Tujuan dari penelitian ini adalah untuk mengetahui jumlah penjualan pada hari berikutnya pada SPBU Ulapato. Berdasarkan uji akurasi menggunakan MAPE, metode ini mengasilkan tingkat akurasi sebesar 92,31 % dengan tingkat error sebesar 7.695 %. Dengan demikian aplikasi yang dihasilkan layak untuk digunakan]]>
<![CDATA[Penerapan Algoritma Naïve Bayes Berbasis Backward Elimination Untuk Prediksi Pemesanan Kamar Hotel ]]>
<![CDATA[Haditsah Annur]]>;
<![CDATA[Sudirman Melangi]]>
<![CDATA[ Jurnal Ilmiah Ilmu Komputer Banthayo Lo Komputer Vol 1 No 1 (2022): Edisi Mei 2022 ]]>
Publisher : <![CDATA[Teknik Informatika Fakultas Ilmu Komputer Universitas Ichsan Gorontalo ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
Full PDF (611.539 KB)
|
DOI: 10.37195/balok.v1i1.99
<![CDATA[Abstract— Hotel is one of the business entities that have the potential to grow and develop which requires a lot of investment that stands both in the middle of the city and in tourist destination areas. Hotel room reservations can be made by customers before they use the hotel via online media, but the problem that occurs is that customers who have made room bookings can cancel orders for various reasons, so that hoteliers feel a loss because the cancellation can provide opportunities for designers to get these customers. This study uses the Naïve Bayes Algorithm as an algorithm that can produce high accuracy and can process a lot of data, and backward selection as the selection of suitable parameter values to improve accuracy. This study uses 10,000 hotel room customer data with accuracy results using the Naïve Bayes Algorithm of 89.67%, and accuracy results using the Naïve Bayes Algorithm and Backward Elemination Feature Selection of 97.83%. Prediction Results Check-Out, Canceled and No-Show.Keywords: Prediction, Booking Room Hotel, Naïve Bayes, Backward Elemination.]]>
<![CDATA[Implementasi Metode Convolutional Neural Network Untuk Identifikasi Citra Digital Daun ]]>
<![CDATA[Asmaul Husnah Nasrullah]]>;
<![CDATA[Haditsah Annur]]>
<![CDATA[ JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 2 (2023): April 2023 ]]>
Publisher : <![CDATA[Universitas Budi Darma ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.30865/mib.v7i2.5962
<![CDATA[Convolutional Neural Network (CNN) is a deep learning algorithm that is widely used to identify and classify a digital image object. In this study the Convolutional Neural Network (CNN) is used as an algorithm that functions to identify leaf types (certain plants) based on images obtained from a public dataset provider named Daun Jamu Indonesia. The existence of image characteristics causes the assistance process to require a more detailed feature selection process. Therefore the CNN method is used in order to solve the problem. The Convolutional Neural Network (CNN) method is capable of performing image recognition by minimizing feature extraction. CNN is also reliable in processing unstructured data because it uses a multi-layered structure of artificial reasoning networks. The image recognition process is carried out by looking for the shape of the model that matches the processed data in order to get the best results. In this study, the augmentation process was carried out on the training data and validation data so that overfitting does not occur in the Convolutional Neural Network (CNN). The results obtained in this study indicate that the Convolutional Neural Network (CNN) method can identify leaf types with a measured accuracy rate of 92% using the Confusion Matrix evaluation method. It is hoped that this research can be used as a reference for the use of the Convolutional Neural Network (CNN) method for image data, especially plant leaf types.]]>
<![CDATA[Analisis Sentimen pada Tweets Divisi Humas Polri Dengan Metode Naive Bayes Classifier ]]>
<![CDATA[Caldiyastovan Mohi]]>;
<![CDATA[Haditsah Annur]]>;
<![CDATA[Roys Pakaya]]>
<![CDATA[ Jurnal Ilmiah Ilmu Komputer Banthayo Lo Komputer Vol 2 No 1 (2023): Edisi Mei 2023 ]]>
Publisher : <![CDATA[Teknik Informatika Fakultas Ilmu Komputer Universitas Ichsan Gorontalo ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.37195/balok.v2i1.509
<![CDATA[News is a story or information about an event that is hotly discussed. It can be utilized by online media to provide updated news to the public. The use of social media among the public in disseminating information is fast so that distance and time are not an obstacle for social media users to be able to receive and access information developments widely without constraints. Twitter is a social media platform that is widely used by individuals, governments, and organizations, including the police, to publish news about related agencies. By applying one of the functions of text mining, namely the Naïve Bayes Classifier to analyze public sentiment towards tweets from the Indonesian Police Public Relations Division’s Twitter account, manually analyzing sentiment on tweets is no longer effective, so a Naïve Bayes Classifier method is needed to automatically analyze sentiment on tweets into positive and negative opinions. So, by using this algorithm, this study gets a fairly high accuracy value. In this study, a high accuracy value of 76% when splitting 10% of testing data and 90% of training data. But when preprocessing and implementing new data in the streamlit framework, it takes up to 1 minute to process the data.]]>
<![CDATA[Optimasi Penempatan dan Kapasitas PLTS on grid Pada Sistem Distribusi Radial Menggunakan Metode Algoritma Genetika Multi Konstrain ]]>
<![CDATA[Muammar Zainuddin]]>;
<![CDATA[Haditsah Annur]]>
<![CDATA[ Jurnal Rekayasa Elektrika Vol 15, No 1 (2019) ]]>
Publisher : <![CDATA[Universitas Syiah Kuala ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
Full PDF (902.812 KB)
|
DOI: 10.17529/jre.v15i1.12507
<![CDATA[The photovoltaic grid-connected business opportunity offers challenges to power quality and voltage fluctuations in the distribution system. The purpose of this study is to optimize the location and power capacity of the grid-connected photovoltaic so that the voltage values are in accordance with the operating standards and meet the requirements of the connection techniques in the distribution system. The distribution system tested is the 69 Buses radial distribution feeder. The optimization uses three constraints namely voltage constraints, active power capacity constraints and total active power capacity constraints of the Grid-Connected Photovoltaic. The Optimization is carried out by considering the value of the percentage of the total active power of the Grid-Connected Photovoltaic capacity to the total load in the distribution system. The optimization results in the first simulation indicated seven grid locations with a total active power of 1.156 MW (38% PV) and produced a fitness value of 0.029. The second Simulation showed 7 locations with a total active power of 1.243 MW (41% PV) and produced a fitness value of 0.023. The Third simulation was identified with 11 locations with a total active power of 1.385 MW (46% PV) and produced a fitness value of 0.022. The best fitness value is the lowest value of the active power losses. The entry of a number of the Grid-Connected Photovoltaic System with distributed location can increase the voltage level in the distribution system.]]>
<![CDATA[Strategi Pengembangan Layanan Informasi Hukum Berbasis Web Desa Kuala Lumpur Kecamatan Paguyaman Kabupaten Boalemo ]]>
<![CDATA[Nur Insani]]>;
<![CDATA[Yusrianto Malago]]>;
<![CDATA[Haditsah Annur]]>
<![CDATA[ ABDIMASKU : JURNAL PENGABDIAN MASYARAKAT Vol 6, No 3 (2023): September 2023 ]]>
Publisher : <![CDATA[LPPM UNIVERSITAS DIAN NUSWANTORO ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.62411/ja.v6i3.1603
<![CDATA[Kegiatan pengabdian bertempat di Desa Kuala Lumpur, Kecamatan Paguyaman, Kabupaten Boalemo. Kegiatan tersebut bertujuan untuk meningkatkan kesejahteraan sumber daya manusia pada desa tersebut. Desa Kuala Lumpur memiliki potensi alam yang belum terekspose, dan kendalanya adalah kurangnya upaya dalam mempromosikan potensi alam dan keterampilan yang dimiliki oleh masyarakat desa tersebut. Sehubungan dengan hal tersebut diperlukan adanya pemberian penyuluhan hukum, sosialisasi, dan pelatihan dengan tema “Strategi Pengembangan Layanan Informasi Hukum Berbasis Web”, maka sangatlah penting untuk menggunakan media sosial dan website dengan bijak dan bertanggungjawab. Hal tersebut dikarenakan bahwa media sosial merupakan sarana yang ampuh dan efektif untuk berbagi informasi dan menjalin komunikasi tanpa mengenal jarak dan waktu, penting juga memperhatikan penerapan kaidah/norma hukum yang berlaku, jika berinteraksi di dunia maya. Dampak dari hasil kegiatan pengabdian, telah menghasilkan nilai yang signifikan terhadap peningkatan kesadaran hukum dan pemahaman pada masyarakat melalui sosialisasi layanan informasi hukum yang berbasis web..]]>
<![CDATA[Optimasi Penempatan dan Kapasitas PLTS on grid Pada Sistem Distribusi Radial Menggunakan Metode Algoritma Genetika Multi Konstrain ]]>
<![CDATA[Muammar Zainuddin]]>;
<![CDATA[Haditsah Annur]]>
<![CDATA[ Jurnal Rekayasa Elektrika Vol 15, No 1 (2019) ]]>
Publisher : <![CDATA[Universitas Syiah Kuala ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.17529/jre.v15i1.12507
<![CDATA[The photovoltaic grid-connected business opportunity offers challenges to power quality and voltage fluctuations in the distribution system. The purpose of this study is to optimize the location and power capacity of the grid-connected photovoltaic so that the voltage values are in accordance with the operating standards and meet the requirements of the connection techniques in the distribution system. The distribution system tested is the 69 Buses radial distribution feeder. The optimization uses three constraints namely voltage constraints, active power capacity constraints and total active power capacity constraints of the Grid-Connected Photovoltaic. The Optimization is carried out by considering the value of the percentage of the total active power of the Grid-Connected Photovoltaic capacity to the total load in the distribution system. The optimization results in the first simulation indicated seven grid locations with a total active power of 1.156 MW (38% PV) and produced a fitness value of 0.029. The second Simulation showed 7 locations with a total active power of 1.243 MW (41% PV) and produced a fitness value of 0.023. The Third simulation was identified with 11 locations with a total active power of 1.385 MW (46% PV) and produced a fitness value of 0.022. The best fitness value is the lowest value of the active power losses. The entry of a number of the Grid-Connected Photovoltaic System with distributed location can increase the voltage level in the distribution system.]]>
<![CDATA[Penerapan Metode Regresi Linier Sederhana Untuk Prediksi Jumlah Persediaan Pestisida ]]>
<![CDATA[Annur, Haditsah]]>;
<![CDATA[Sudirman Melangi]]>;
<![CDATA[Andri Setiawan]]>
<![CDATA[ Jurnal Ilmiah Ilmu Komputer Banthayo Lo Komputer Vol 3 No 1 (2024): Mei 2024 ]]>
Publisher : <![CDATA[Teknik Informatika Fakultas Ilmu Komputer Universitas Ichsan Gorontalo ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.37195/balok.v3i1.863
<![CDATA[Abstract - Forecasting is needed to make things easier for CV. Anak Tani predict how much Roger's pesticide supply will be needed in the following month, so that they do not experience the delivery of the amount of Roger's pesticide supply in the following month. The simple linear regression method is a forecasting method that uses two factors so that it can determine maximum results. The problem faced is that there is often a buildup of pesticide 1 liter Roger 480SL inventory CV. Anak Tani. The aim is to find out the results of applying a simple linear regression method to predict the amount of inventory of 1 Liter Roger 480SL pesticide located at CV. Anak Tani. The results obtained by the mean absoluter percentage error (MAPE) were tested with the 2022 data obtained, namely 7.80%, so it can be concluded that this system is effective to use.Key words: Forecasting, Inventory, Pesticide Roger 480sl 1 Liter, Mape]]>
<![CDATA[PENERAPAN ALGORITMA NAIVE BAYES BERBASIS FORWARD SELECTION UNTUK MEMPREDIKSI PENJUALAN MOBIL BEKAS ]]>
<![CDATA[annur, haditsah]]>;
<![CDATA[Moh.Efendi Lasulika]]>
<![CDATA[ JURNAL ILMU KOMPUTER Vol 10 No 2 (2024): Edisi September ]]>
Publisher : <![CDATA[LPPM Universitas Al Asyariah Mandar ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.35329/jiik.v10i2.320
<![CDATA[Cars are one of the vehicles that are the daily needs of the people, not only the use of new cars is in demand, but now used cars are also in great demand because the quality of used cars is still good and the many types of used cars are sold in the market. The aim of the researchers is to increase public interest in switching to buying used cars. This study uses data mining methods, one of which is prediction using the Naive Bayes algorithm as an algorithm that uses probabilistic and statistical methods to predict the future, besides that the data is also processed using forward selection feature selection which aims to reduce the level of complexity of a classification algorithm while increasing accuracy. The research data used were 2318 records, in this study an experiment was carried out with the accuracy results obtained using split validation on the naive Bayes algorithm of 96.98% and then another experiment was carried out to obtain accurate results using split validation on the naive bayes algorithm based on forward selection of 97.82 %. Thus the naive Bayes algorithm based on forward selection is suitable for predicting, as well as being used for handling in the future that there are still many used cars that are of interest to the public..]]>
<![CDATA[PENERAPAN NETWORK MONITORING SYSTEM (NMS) SECARA VISUAL PADA INFRASTRUKTUR JARINGAN FISIK BERBASIS WEB ]]>
<![CDATA[Annur, Haditsah]]>;
<![CDATA[Laari, Ramdan A]]>
<![CDATA[ Nusantara of Engineering (NOE) Vol 5 No 2 (2022): Volume 5 No 2 Tahun 2022 ]]>
Publisher : <![CDATA[Universitas Nusantara PGRI Kediri ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.29407/noe.v5i2.18682
<![CDATA[A Network Monitoring System (NMS) is a system that continuously monitors the network and provides immediate notification to network administrators in the event of problems or interruptions. This study aims to apply a Network Monitoring System for monitoring network devices in Universitas Ichsan Gorontalo. This application is visually web-based, so network administrators can easily find problems with network devices in this system. As a result of this study, the system can now find problematic network devices on existing network devices. It can show the results in a visual format. If the network device check fails, the system recognizes if there is a problem with the network device.]]>
