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All Journal Jurnal Edukasi dan Penelitian Informatika (JEPIN) ILKOM Jurnal Ilmiah Jurnal Nasional Komputasi dan Teknologi Informasi JOURNAL OF SCIENCE AND SOCIAL RESEARCH Simtek : Jurnal Sistem Informasi dan Teknik Komputer Jurnal Tecnoscienza Jurnal Indonesia : Manajemen Informatika dan Komunikasi International Journal Software Engineering and Computer Science (IJSECS) Jurnal Nasional Cosphi Nusantara of Engineering (NOE) Jurnal Ilmiah Ilmu Komputer Banthayo Lo Komputer (BALOK) Journal Of Informatics And Busisnes Empiris Jurnal Pengabdian Pada Masyarakat (EJPPM) Jurnal Indonesia : Manajemen Informatika dan Komunikasi
Andi Bode
Universitas Ichsan Gorontalo
Aerospace Engineering Automotive Engineering Biochemistry, Genetics & Molecular Biology Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Mechanical Engineering Neuroscience Social Sciences Transportation Other

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The K-Nearest Neighbor Algorithm using Forward Selection and Backward Elimination in Predicting the Student’s Satisfaction Level of University Ichsan Gorontalo toward Online Lectures during the COVID-19 Pandemic Andi Bode; Zulfrianto Y Lamasigi; Ivo Colanus Rally Drajana
ILKOM Jurnal Ilmiah Vol 15, No 1 (2023)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v15i1.1381.118-123

Abstract

Academic services are actions taken by state and private universities to provide convenience for student’s academic activities. During the current covid-19 pandemic, every university remains active in academic activities. This study aimed to apply the K-Nearest Neighbor algorithm in predicting the level of student satisfaction with online lectures at University Ichsan Gorontalo. Our main aim was to obtain quantitative information to measure student satisfaction with online lectures during the pandemic, which should be taken into account when making decisions. K-Nearest Neighbor is a non-parametric Algorithm that can be used for classification and regression, but K-Nearest Neighbor are better if feature selection is applied in selecting features that are not relevant to the model. Feature Selection used in this research is Forward Selection and Backward Elimination. Seeing the results of experiments that have been carried out with the application of the K-nearest Neighbor algorithm and the selection feature, the results of the forecasting can be used for consideration or policy in decision making. The highest level of accuracy in the K-Nearest Neighbor algorithm model used Forward Selection with an accuracy rate of 98.00%. Thus, the experimental results showed that feature selection, namely forward selection, was a better model in the relevant selection variables compared to backward elimination.
Implementasi Eoip Tunnel Dan Bonding Di Routerboad Mikrotik Untuk Menambah Kapasitas Wireless Link Di Pt Gomeds Network Dedi Setiawan; Andi Bode; Warid Yunus
Jurnal Ilmiah Ilmu Komputer Banthayo Lo Komputer Vol 2 No 1 (2023): Edisi Mei 2023
Publisher : Teknik Informatika Fakultas Ilmu Komputer Universitas Ichsan Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37195/balok.v2i1.397

Abstract

Information technology in the world has been experiencing rapid development, and Indonesia is no exception. In Indonesia, internet access is increasingly widespread in remote villages provided by ISPs. Precisely on the island of Sulawesi, there are several ISPs, one of which is PT Gomeds Network. PT Gomeds Network is a company engaged in internet network provider services. It was founded on January 17, 2011, in Gorontalo. It has infrastructure spread throughout the island of Sulawesi. PT Gomeds Network utilizes wireless network technology. Each Base Transceiver Station (BTS) has a different local link capacity. The problem experienced by PT Gomeds Network is the insufficient capacity of the BTS wireless link and the absence of a backup Wireless Link at each BTS when the Wireless Radio device or the main link line connecting the BTS is lost or damaged. The purpose of this study is to increase the wireless link capacity of BTS so that customers connected to BTS can receive services based on the internet package rented and provide benefits in the form of a backup wireless link when one of the wireless link lines is down due to damage. The results of this study explain that the EOIP Tunnel and Bonding methods can run as expected and produce adequate BTS capacity, and backup wireless links for BTS can function without disconnecting wireless links.
COMPARASI ALGORITMA FORECASTING SVM, K-NN DAN NN UNTUK PREDIKSI HARGA CABAI KOTA GORONTALO Abdul Yunus Labolo; Andi Bode; Ivo Colanus Rally Drajana; Jorry Karim
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol 6, No 2 (2023): June 2023
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v6i2.1112

Abstract

The high demand for chilies, especially in Gorontalo, is a driving force for chilli cultivating farmers. The price of chili which is uncertain every day can fluctuate. The Gorontalo City Food Service cannot make predictions to estimate prices in the following month. Prediction is defined as the use of statistical techniques in the form of a picture of the future based on the processing of historical figures. Due to the many algorithms that can be used in predictions, this study will compare forecasting algorithms namely Support Vector Machine (SVM), K-Nearest Neighbor (K-NN) and Neural Network (NN). Experiments that have been carried out, on chili price prediction with forecasting algorithms have been successfully carried out. The root mean square error (RMSE) result of the SVM algorithm is 0.233, the K-NN algorithm is 0.223 and the NN algorithm is 0.206. Of the three forecasting algorithms used, the best results are produced by the Neural Network algorithm with the smallest RMSE value of 0.206. So it can be concluded that the proposed model is close to perfection, because a comparison of the results of implementing chili price predictions for the next three months produces an accuracy value of 99.25% on average
Sistem Pendukung Keputusan Bantuan Rumah Rehab Menggunakan Metode Composite Performance Index Merin Nurlaisa Abbas; Ivo Colanus Rally Drajana; Andi Bode
Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI) Vol 5, No 6 (2022): Desember 2022
Publisher : Program Studi Teknik Komputer, Fakultas Teknik. Universitas Serambi Mekkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/jnkti.v5i6.5152

Abstract

Abstrak - Kemajuan suatu daerah dapat dilihat dari kesejahteraan warganya baik dari segi ekonomi, pendidikan, kesehatan dan lingkungan. Di daerah berkembang, masih banyak ketidak merataan kesejahteraan yang menyebabkan masih banyaknya warga miskin yang membutuhkan bantuan dari pemerintahan. Berdasarkan data Dinas Sosial tingkat kemiskinan masyarakat khususnya di Desa Limbula sebesar 40% sehingga banyak masyarakat miskin yang menempati rumah tidak layak huni. Dengan program rumah rehab yang diprogramkan oleh pemerintah semoga masyarakat Desa Limbula dapat menikmati manfaat khususnya bagi masyarakat miskin. Didasarkan pada hal tersebut maka dibutuhkan suatu system yang dapat membantu dalam menentukan penerima program rumah rehab. Untuk memberikan solusi terhadap permasalahan yang ada pada penelitian ini maka Sistem Pendukung Keputusan menggunakan metode Composite Performance Index (CPI) adalah salah satu solusi yang dapat memudakan prosedur pengambilan keputusan untuk memberikan bantuan rumah rehab kepada masyarakat desa Limbula. Dengan adanya sistem pendukung keputusan ini, penerima bantuan benar-benar dipilih oleh aplikasi ini  sehingganya akan mengurangi nepotisme atau kecurangan dalam menentukan masyarakat penerima bantuan. Berdasarkan hasil pengujian white box dan black box sistem pendukung keputusan penerima bantuan rumah rehab dapat diterapkan secara maksimal di desa Limbula.Kata Kunci: SPK, CPI, Bantuan, Rumah Rehab Abstract - The progress of an area can be seen from the welfare of its citizens in terms of economy, education, health and the environment. In developing areas, there is still a lot of inequality in welfare which causes many poor people to need assistance from the government. Based on data from the Social Service, the poverty rate for the community, especially in Limbula Village, is 40% so that many poor people live in uninhabitable houses. With the rehabilitation house program programmed by the government, it is hoped that the people of Limbula Village can enjoy the benefits, especially for the poor. Based on this, we need a system that can assist in determining the beneficiaries of the rehabilitation house program. To provide solutions to the problems that exist in this study, the Decision Support System using the Composite Performance Index (CPI) method is one of the solutions that can facilitate decision-making procedures for providing rehabilitation housing assistance to the Limbula village community. With this decision support system, beneficiaries are actually selected by this application so that it will reduce nepotism or fraud in determining the beneficiary community. Based on the results of white box and black box testing, the decision support system for beneficiaries of rehabilitation housing assistance can be maximally implemented in Limbula village.Keywords: SPK, CPI, Aid, Rehab House
Prediksi Status Penderita Stunting Pada Balita Provinsi Gorontalo Menggunakan K-Nearest Neighbor Berbasis Seleksi Fitur Chi Square Ivo Colanus Rally Drajana; Andi Bode
Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI) Vol 5, No 2 (2022): April 2022
Publisher : Program Studi Teknik Komputer, Fakultas Teknik. Universitas Serambi Mekkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/jnkti.v5i2.4205

Abstract

Abstrak - Stunting adalah malnutrisi yang ditandai dengan tinggi badan, diukur dengan standar deviasi dari WHO. Dinas Kesehatan Provinsi Gorontalo khususnya dibidang Gizi mengenai stunting, selama ini melakukan kegiatan pemantauan tiap-tiap puskesmas dan posyandu. Pemantauan dan pendataan terkait stunting di berbagai puskesmas faktor penting. Masalah yang sering muncul adalah data yang dikumpulkan untuk underestimasi selalu tidak akurat setiap bulannya, karena hanya perkiraan yang dihitung berdasarkan kasus puskesmas. Prediksi yang akurat diperlukan untuk mengatasi permasalahan yang ada. Penelitian ini menggunakan algoritma K-Nearest Neighbor (K-NN) menggunkan Chi Square. Berdasarkan hasil eksperimen, prediksi jumlah penderita stunting telah berhasil dilakukan. Maka nilai hasil dari prediksi tersebut dapat diimplementasikan untuk bahan pertimbangan atau kebijakan didalam pengambilan keputusan. Tingkat error terkecil hasil RMSE 1,200 pada algoritma K-Nearest Neighbor menggunkan Chi Square dibandingkan algoritma K-Nesrest Neighbor tanpa seleksi fitur. Dengan demikian dari hasil eksperimen menunjukan bahwa penambahan seleksi fitur telah menunjukan performa kinerja yang baik pada algoritma K-Nearest Neighbor.Kata kunci: Prediksi, Stunting, K-NN, Chi Square Abstract - Stunting is a nutritional deficiency characterized by height as measured by the WHO standard deviation. The Gorontalo Provincial Health Office, especially in the field of nutrition related to stunting, has so far carried out monitoring activities at every puskesmas and posyandu. Monitoring and data collection related to stunting in various health centers is an important factor. The problem that often arises is that the data collected for underestimation is always inaccurate every month, because only estimates are calculated based on puskesmas cases. Accurate predictions are needed to overcome the existing problems. This study uses the K-Nearest Neighbor (K-NN) algorithm using Chi Square. Based on the experimental results, the prediction of the number of stunting sufferers has been successfully carried out. Then the value of the predicted results can be implemented for consideration or policy in decision making. The smallest error rate is RMSE 1,200 in the K-Nearest Neighbor algorithm using Chi Square compared to the K-Nesrest Neighbor algorithm without feature selection. Thus, the experimental results show that the addition of feature selection has shown good performance on the K-Nearest Neighbor algorithm.Keyword: Prediction, Stunting, K-NN, Chi Square
Sistem Informasi Pemerintah Daerah Dalam Pelayanan Berbasis E-Government Kabupaten Banggai Laut (Studi Kasus Dinas Kependudukan Dan Pencatatan Sipil) Bode, Andi; Udilawaty, Siska; Harun, Rofiq; Talodo, Moh. Arif N
Journal Of Informatics And Busisnes Vol. 2 No. 2 (2024): Juli - September
Publisher : CV. ITTC INDONESIA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jibs.v2i2.1468

Abstract

The Population and Civil Registration Service of Banggai Laut Regency is a regional government agency that serves the community, one of which is civil registration of population in the issuance of documents. Currently, the Department of Population and Civil Registration still uses a manual system to carry out the recording process. Before issuing the birth certificate quotation document, the applicant must register manually, in this case it is considered ineffective in providing public services. To overcome the above, it is necessary to create a new system, namely a web-based public service information system that can be accessed online by the public, so that it is hoped that it can simplify administration for the people of Banggai Laut, especially those who live on the islands. So the use of information technology as a media source for public administration services that are fast and easy to understand can be a solution for optimizing the population administration service process in Banggai Laut Regency. Based on these problems, making the creation of a Regional Government Information System in E-Government Based Services in Banggai Laut Regency (Case Study of the Population and Civil Registration Service) is the right solution in public services to make it easier for the public to manage population administration and civil registration to make it more effective and efficient.
K-Nearest Neighbor for Gorontalo City Chili Price Prediction Using Feature Selection, Backward Elimination, and Forward Selection Labolo, Abdul Yunus; Utiarahman, Siti Andini; Lasulika, Mohamad Efendi; Drajana, Ivo Colanus Rally; Bode, Andi
International Journal Software Engineering and Computer Science (IJSECS) Vol. 3 No. 3 (2023): DECEMBER 2023
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v3i3.1709

Abstract

This study addresses chili price volatility, an important concern that impacts the national economy and societal welfare. Fluctuations in chili prices in the retail market greatly influence market demand, thereby influencing farming decisions, especially chili cultivation. To help make better decisions, Researchers use forecasting, which is defined as the projection of future trends based on the analysis of historical data, using statistical methods. The K-Nearest Neighbor (K-NN) algorithm is used because of its resistance to high noise on large training datasets. However, challenges arise in determining the optimal value of 'k' and selecting related attributes. To overcome this, Feature Selection is applied to refine the model by removing irrelevant features, resulting in a significant reduction in the model error rate. This improvement indicates an increase in the efficiency of the K-NN algorithm with the incorporation of Feature Selection. Our findings show that the model, with backward elimination in Feature Selection, achieves a Root Mean Square Error (RMSE) of 0.202, outperforming the model using forward selection. The prediction accuracy of this model reaches an average of 78.86%, which is much higher than the baseline data of 50%. This shows the success of the proposed method in predicting chili prices.
Klasifikasi Klasifikasi Jenis Buah Tomat Menggunakan Convolutional Neural Network Ahmad; Idris, Irma Surya Kumala; Bode, Andi
Jurnal Ilmiah Ilmu Komputer Banthayo Lo Komputer Vol 2 No 2 (2023): November 2023
Publisher : Teknik Informatika Fakultas Ilmu Komputer Universitas Ichsan Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37195/balok.v2i2.617

Abstract

Abstrac ; Some Indonesian people utilize food sources evenly. Tomatoes are known to have very good nutritional content so people can consume them every day. Many species/types of tomatoes have high similarity so it is difficult to distinguish them. Tomato fruit type recognition in this study employs Convolutional Neural Network. The stages of the method used are feature learning and classification. To classify tomato fruit types, the CNN network is trained with image training data. The training process is carried out by looking for a form of model that is following the data to be processed to get the best results. It is also used in the argumentation process on training and validation data so that overfitting does not occur in the CNN network. The experimental results show that the convolutional Neural Network method can recognize tomato types with an accuracy rate of 96.6%, recall of 100%, precision of 96.6%, and an F-1 Score of 96.28% of 30 images using Confusion Matrix testing.   Keywords: classification, tomato fruit type, Convolutional Neural Network
Analisa Kualitas Layanan Jaringan Internet Pada Wireless Lan Menggunakan Metode Qos (Quality Of Service) Tangahu, Rizqi Adiputera; Andi Bode; Sunarto Taliki
Jurnal Ilmiah Ilmu Komputer Banthayo Lo Komputer Vol 3 No 1 (2024): Mei 2024
Publisher : Teknik Informatika Fakultas Ilmu Komputer Universitas Ichsan Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37195/balok.v3i1.723

Abstract

Abstract- The development of the Internet network is now very rapid. The Internet is a source of information that people mostly use to find information they need. It is not only used in big cities but also remote villages. The internet network must have a high access network speed to attract many users, whether or not in public facilities such as terminals or city parks. In other words, the internet network can make a big contribution to society. Currently, the internet has become a trending need, starting from the world of business, education, government, entertainment, and others. Kedai Mako is a coffee shop that uses the internet network as a means to make customers feel at home apart from its delicious coffee. Through internet networks, customers enjoy their coffee while playing games or social media without using their data quota. Kedai Mako customers using the internet network usually only know whether or nonot t the network is good by measuring the network speed via YouTube or online games. Yet, Internet users do not know whether or not the quality of the internet service they receive is good. It has not implemented QoS or measured network quality using QoS. The internet network at Kedai Mako often goes down, making customers uncomfortable with the internet network at Kedai Mako. Therefore, this research manages to analyze the internet network at Kedai Mako, and the results of the analysis can be used as recommendations for the physical implementation of the internet network hoped to be able to support the addition of other supporting services in the future. Based on the results of Quality of Service measurements in 5 experiments, the average value for the Throughput index obtained is 3,4 in the Very Good category, the index value for Packet Loss gained is 3.6 in the Very Good category, the index value for Delay analyzed is 4 in the Very Good category, and the Jitter index value indicates 3, in the Good category. The average index value for Kedai Mako is 3.5 in the Good category. Keywords: QoS, Wireshark, WLAN, Throughput, Packet Loss, Delay, Jitter
K-NEAREST NEIGHBOR MENGGUNAKAN FEATURE SELECTION BACKWARD ELIMINATION UNTUK PREDIKSI JUMLAH PERMINTAAN DARAH PADA PMMI KOTA GORONTALO Lasena, Yulianti; Taliki, Sunarto; Lasulika, Mohamad Efendi; Bode, Andi
Jurnal Indonesia : Manajemen Informatika dan Komunikasi Vol. 4 No. 1 (2023): Januari
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Indonesia Banda Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jimik.v4i1.172

Abstract

The importance of the availability of blood at PMI, it is expected that PMI always maintains the amount of blood supply to meet the need for blood transfusions. Prediction of blood supply is needed to overcome problems related to bloodstock supply at PMI Gorontalo. The application of predicting the number of blood requests with the K-Nearest Neighbor Algorithm can be done to overcome the existing problems. K-NN is a non-parametric algorithm that can be used for classification and regression. The last few decades have been used in prediction cases, but the K-NN algorithm is better if feature selection is applied in selecting features that are not relevant to the model, the feature selection used in this study is Backward Selection. This study aims to determine the error value in predicting the number of requests for blood at the PMI in Gorontalo City. Meanwhile, the purpose of this research is to find the error value of the K-Nearest Neighbor Algorithm and Feature Selection which can be used as a reference for PMI in making policies to make various efforts to maintainbloodstockk in the future.
Co-Authors Abd Rahmat karim Haba Abdul Yunus Labolo Ahmad Asdiranto A Marisang Azminuddin I. S. Azis Balok, Admin Budy Santoso Dedi Setiawan Djailani, Moh. Adrian Drajana, Ivo Colanus Rally Fatma YR Dunggio Idris, Irma Surya Kumala Ikbal Iskandar Irma Surya Kumala Idris Irvan A. Salihi Ivo Colanus Rally Drajana Jorry Karim Labolo, Abdul Yunus Merin Nurlaisa Abbas Mochammad Sakir Moh. Yasir Mohamad Efendi Lasulika Mooduto, Sarlis Muhamad, Azhar Muis Nanja Nur, Muh Jabal Nur, Muh. Jabal Posumah, Risky Randa Ramadhani, Syarifah Fitrah Rofiq Harun Sarlis Mooduto Serwin Siska Udilawaty Sunarto Taliki Swastiani Dunggio Syahrir Abdussamad Talodo, Moh. Arif N Tangahu, Rizqi Adiputera Utiarahman, Siti Andini Warid Yunus Yasin Aril Mustofa Yulianti Lasena Yurike Musa Zohrahayaty Zulfrianto Yusrin Lamasigi