Jurnal Ilmiah Ilmu Komputer Banthayo Lo Komputer (BALOK)
JOURNAL ILMIAH (Banthayo Lo Komputer) BALOK encompasses all aspects of the latest outstanding research and developments in the field of computer science including; Artificial Intelligence, Software Enginering, Data Mining, Computer Networks, Internet of Things,
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<![CDATA[Prediksi Penjualan Tempered Glass Handphone Xiaomi Menggunakan Metode Least Square ]]>
<![CDATA[Fahriansyah Patompo]]>;
<![CDATA[Irvan Abraham Salihi]]>
<![CDATA[ Jurnal Ilmiah Ilmu Komputer Banthayo Lo Komputer Vol 1 No 2 (2022): Edisi November (2022) ]]>
Publisher : <![CDATA[Teknik Informatika Fakultas Ilmu Komputer Universitas Ichsan Gorontalo ]]>
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DOI: 10.37195/balok.v1i2.111
<![CDATA[ABSTRACT The Amanda Cell store is a Xiaomi handphone tempered glass sales store. So far, the Amanda Cell Store has not been able to predict the ups and downs of Xiaomi cell phone tempered glass sales. This problem affects the ordering process for goods while in fact, some have to wait for sold-out sales. It is a result of the emptiness of tempered glass on other cellphone brands and the cumulation of unsold tempered glass causing some losses. To overcome the problems, an application that can perform a prediction to find out the types of Tempered Glass Xiaomi Mobile is required. The purpose of this study is to find out the results of applying the Least Square Method to predict sales of Tempered Glass for Xiaomi Mobile Phones and to know the level of accuracy of Xiaomi's Tempered Glass sales prediction using the Least Square method. This study results in a conclusion that June 2022 's Prediction is 27,037 and July 2022's prediction is 7,087 Keywords: Sales Prediction, Least Square]]>
<![CDATA[Prediksi Harga Cabai Di Kota Gorontalo Menggunakan Metode Weighted Moving Average ]]>
<![CDATA[Siska M. Igirisa]]>;
<![CDATA[Amiruddin Amiruddin]]>
<![CDATA[ Jurnal Ilmiah Ilmu Komputer Banthayo Lo Komputer Vol 1 No 2 (2022): Edisi November (2022) ]]>
Publisher : <![CDATA[Teknik Informatika Fakultas Ilmu Komputer Universitas Ichsan Gorontalo ]]>
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DOI: 10.37195/balok.v1i2.117
<![CDATA[This study aims to describe the price of chili through technology using the Weighted Moving Average method. Chili is one of the food commodities that can affect the value of inflation. The price of chili that is less erratic and even tends to continue to increase at certain times will have a bad impact on the country and society. The on-demand chili is a type of Gorontalo local chili. Chili is one of the food products that have very fluctuating prices. The Weighted Moving Average method has a good is believed to be good in predicting chili prices at the Gorontalo City Food Service Office. At the accuracy level, the prediction of future chili prices can use the Weighted Moving Average method. It is found that the application of the Weighted Moving Average method in building a system of chili price prediction can be seen through the MAPE results. The lowest error rate indicates the third-month moving average prediction (n=3), namely 26.48%, or the accuracy rate is 73.52%. Thus, the moving average value used in the chili price prediction is an average of 3 months (n=3). Keywords: prediction, chili price, Weighted Moving Average, MAPE]]>
<![CDATA[Prediksi Jumlah Produksi Ikan Asin Menggunakan Metode Regresi Linear Sederhana ]]>
<![CDATA[M Rajab Mudatsir]]>;
<![CDATA[Serwin Serwin]]>
<![CDATA[ Jurnal Ilmiah Ilmu Komputer Banthayo Lo Komputer Vol 1 No 2 (2022): Edisi November (2022) ]]>
Publisher : <![CDATA[Teknik Informatika Fakultas Ilmu Komputer Universitas Ichsan Gorontalo ]]>
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DOI: 10.37195/balok.v1i2.143
<![CDATA[CV. Tirta Tenggiri produces salted fish caught from the sea or local fishermen. The production is to meet the local and outside demands of the Pohuwato District. Even though market demand for salted fish is always available, CV. Tirta Tenggiri is sometimes not able to adjust the production of salted fish with consumer demand. To obtain the information about the total production of salted fish for better development of the company in the future, predictions are made by using the simple linear regression method. The variables used include raw materials and the total demand (X) and land area as a prediction result (Y). The dataset in this study for the prediction of salted fish production takes data from 2019 to 2021. The results show that the number of salted fish to produce in May 2022 obtains a value of y = 168.53 + 24.175. The accuracy with MAPE is 0.8%. Testing the system by using a white box that has the same value of V(G) and CC, namely =2.Keywords: prediction, salted fish production, simple linear regression, White Box and Black Box Testing]]>
<![CDATA[Prediksi Permintaan Kantong Darah Berdasarkan Golongan Darah Menggunakan Metode Single Moving Average (SMA) ]]>
<![CDATA[Isran Mertosono]]>;
<![CDATA[Yasin Mustofa]]>
<![CDATA[ Jurnal Ilmiah Ilmu Komputer Banthayo Lo Komputer Vol 1 No 2 (2022): Edisi November (2022) ]]>
Publisher : <![CDATA[Teknik Informatika Fakultas Ilmu Komputer Universitas Ichsan Gorontalo ]]>
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DOI: 10.37195/balok.v1i2.147
<![CDATA[Blood donation is blood taking process from any volunteer to store in a blood bank for blood transfusion purposes. Blood donation is usually conducted routinely at the Blood Transfusion Unit. The demand for blood is estimated to be higher due to the rapid development of medical science. The rapid development of medical science requires a lot of blood needs, one of which is in organ transplant procedures. The prediction system for blood bag demand can be developed using the Single Moving Average method. The Moving Average method is suitable for long-term data. A single moving average is a forecast for a future period that requires historical data over a certain period. The level of accuracy in predicting blood demand is categorized as quite Good, with an accuracy rate of 75% with MAPE testing of 25%. The accuracy results show that the application designed is feasible to predict the demand for blood bags by adding the amount of data that can optimize the Single Moving Average method to produce more precise and accurate predictions.Keywords: Single Moving Average (SMA), blood demand prediction, MAPE]]>
<![CDATA[Penerapan Convolutional Neural Network (CNN) Untuk Klasifikasi Penggunaan Masker ]]>
<![CDATA[Rifdah Rofifah Faruk Abdullah]]>;
<![CDATA[Maryam Hasan]]>
<![CDATA[ Jurnal Ilmiah Ilmu Komputer Banthayo Lo Komputer Vol 1 No 2 (2022): Edisi November (2022) ]]>
Publisher : <![CDATA[Teknik Informatika Fakultas Ilmu Komputer Universitas Ichsan Gorontalo ]]>
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DOI: 10.37195/balok.v1i2.164
<![CDATA[ABSTRACT RIFDAH ROFIFAH FARUK ABDULLAH. T3117114. APPLICATION OF CONVOLUTIONAL NEURAL NETWORK FOR MASK USE CLASSIFICATION The use of masks is a part of the comprehensive series of prevention and control measures that can limit the spread of certain respiratory viral diseases. Masks can be used both to protect healthy people (worn to protect themselves when in contact with an infected person) and to control the sources of prevention (to be worn by an infected person to prevent further transmission). The problem that often occurs is that many people use masks, but improperly or inappropriately. For instance, the right thing, someone uses a mask to cover the mouth and nostrils. Therefore, an application for the use of computer-assisted mask detection is made. It can detect the use of a person's mask so that the right and wrong use categories are obtained that represent it by capturing it in an image. It is done by using the Convolutional Neural Network method. In the classification stage, the accuracy results are 60- 70%. Keywords: classification, masks use, Convolutional Neural Network]]>
<![CDATA[Identifikasi Kualitas Udang Segar Menggunakan Metode Gray Level Co-Occurance Matrix dan Artificial Neural Network : - ]]>
<![CDATA[Wanda Aprilia Pangemanan]]>;
<![CDATA[Irma Surya Kumala Idris]]>
<![CDATA[ Jurnal Ilmiah Ilmu Komputer Banthayo Lo Komputer Vol 1 No 2 (2022): Edisi November (2022) ]]>
Publisher : <![CDATA[Teknik Informatika Fakultas Ilmu Komputer Universitas Ichsan Gorontalo ]]>
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DOI: 10.37195/balok.v1i2.168
<![CDATA[This study is conducted to know the fresh shrimp quality. In this study, the data collection is through images of shrimp of a variety of sizes and with the number of classes. There are two classes, namely fresh and not fresh. This study is observed independently. The methods used in this study are the Gray Level Co-occurrence Matrix and Artificial Neural Network methods. The performance of using the GLCM and ANN methods in the identification process of fresh shrimp quality indicates a very good performance as proven by the accuracy of 93%, recall of 100%, the precision of 90%, and an F1 score of 95%. Keywords: fresh shrimp quality, GLCM, ANN]]>
<![CDATA[Clustering Tingkat Ekonomi Mahasiswa Calon Penerima Kartu Indonesia Pintar (KIP) Kuliah Metode K-Means ]]>
<![CDATA[Maimunsuyatni Sompa]]>;
<![CDATA[Rezqiwati Ishak]]>
<![CDATA[ Jurnal Ilmiah Ilmu Komputer Banthayo Lo Komputer Vol 1 No 2 (2022): Edisi November (2022) ]]>
Publisher : <![CDATA[Teknik Informatika Fakultas Ilmu Komputer Universitas Ichsan Gorontalo ]]>
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DOI: 10.37195/balok.v1i2.175
<![CDATA[ABSTRACTThe Smart Indonesia Card (KIP) for Higher Education by the government is under the auspices of theMinistry of Education and Culture. The Smart Indonesia Card (KIP) for Higher Education aims to helpprovide tuition assistance, especially for poor students to continue their studies. It prevents children fromdropping out of education. Universitas Ichsan Gorontalo is one of the private universities granted a quotaof the Smart Indonesia Card (KIP) for Higher Education. The limited number of student admissions (quota)of the Smart Indonesia Card (KIP) for Higher Education requires special attention in determining the rightstudents as recipients on target to get the number of quotas that are not commensurate with the number ofapplicants. In seeing that, clusters are carried out based on the economic level of students to get a groupof students prioritized to get the Smart Indonesia Card (KIP) for Higher Education. The K-Means methodgets clustering results using the Elbow technique, namely 5 clusters. The results of clustering for eachcluster indicate that Cluster 1 is a group of students with medium economic level and taken the secondpriority for recipients of assistance. Cluster 2 is a group of students with low economic levels and becomesthe first priority of recipients of assistance. Cluster 3 is a group of students with middle to high economiclevels and becomes the third priority for recipients of assistance. Cluster 4 is a group of students withmiddle economic level and become the fourth priority for recipients of assistance. Cluster 5 is a group ofstudents with middle to upper economic levels and is the fifth priority for recipients of assistance.Keywords: The Smart Indonesia Card (KIP) for Higher Education, Clustering, Elbow, K-Means]]>
<![CDATA[Rancang Bangun Prototype Sistem Pendeteksi Banjir Menggunakan Thingspeak Dan Esp8266 ]]>
<![CDATA[Muhammad Adnan Gobel]]>;
<![CDATA[Abdul Rahmat Karim Haba]]>
<![CDATA[ Jurnal Ilmiah Ilmu Komputer Banthayo Lo Komputer Vol 1 No 2 (2022): Edisi November (2022) ]]>
Publisher : <![CDATA[Teknik Informatika Fakultas Ilmu Komputer Universitas Ichsan Gorontalo ]]>
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DOI: 10.37195/balok.v1i2.255
<![CDATA[Banjir adalah bagian dari permasalahan lingkungan fisik di permukaan bumi yang mengakibatkan kerugian dan dapat diartikan suatu keadaan di mana air sungai melimpah, menggenangi daerah sekitarnya sampai kedalaman tertentu hingga menimbulkan kerugian. Sistem Cerdas merupakan bagian dari bidang Ilmu Komputer/Informatika dan Rekayasa Cerdas untuk pengembangan berbagai metode bekemampuan tinggi yang diilhami oleh fenomena alam untuk menyelesaikan berbagai masalah kompleks di dunia nyata. Dalam penelitian ini sistim cerdas digunakan untuk mendeteksi ketinggian permukaan air sungai dengan menggunakan sensor Ultrasonik, Mikrokontroler ESP8266, dan Thingspeak. Sensor ultrasonik adalah sensor yang bekerja berdasarkan prinsip pantulan gelombang suara dan digunakan untuk mendeteksi keberadaan suatu objek tertentu di depannya. Kata Kunci: pendeteksi banjir, sensor ultrasonik, Mikrokontroler ESP8266, Thingspeak]]>
<![CDATA[Pengelompokan Tingkat Kerusakan Hutan Menggunakan Algoritma K-Means Clustering ]]>
<![CDATA[Fadila Badu]]>;
<![CDATA[Asmaul Husnah Nasrullah]]>
<![CDATA[ Jurnal Ilmiah Ilmu Komputer Banthayo Lo Komputer Vol 1 No 2 (2022): Edisi November (2022) ]]>
Publisher : <![CDATA[Teknik Informatika Fakultas Ilmu Komputer Universitas Ichsan Gorontalo ]]>
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DOI: 10.37195/balok.v1i2.276
<![CDATA[Kerusakan sumber daya hutan mengakibatkan penurunan kemampuan fungsi hutan dalam mendukung segala aspek kehidupan. Faktor yang mengakibatkan terjadinya tingkat kekritisan hutan, salah satunya adalah pertumbuhan penduduk yang begitu cepat, serta aktivitas pembangunan dalam berbagai bidang tentu saja akan menyebabkan ikut meningkatnya permintaan akan lahan. Oleh karenanya Dinas Kehutanan dan pertambangan Kabupaten Bone Bolango sangat memerlukan data yang akurat terhadap data kerusakan hutan yang terjadi setiap saat. Untuk itu penelitian ini bertujuan untuk merealisasikan penggunaan metode K-Means cluster yang mampu memberikan pengelompokan tingkat kerusakan hutan, sehingga dapat menjadi referensi bagi Dinas Kehutanan dan pertambangan Kabupaten Bone Bolango dalam membuat keputusan secara cepat dan tepat.Selaras dengan masalah yang dihadapi, peneliti memandang perlunya suatu tindakan Pengelompokan Tingkat Kerusakan Hutan. pengelompokkan tersebut dilakukan dengan menerapkan sebuah Metode K-Means Clustering. Dari hasil penelitian yang telah dilakukan menunjukkan metode K-Means mampu mengelompokkan tingkat kerusakan hutan dengan baik, hal itu dapat dilihat diperolehnya tiga kelompok kerusakan hutan yakni kerusakan sedang, menengah dan kerusakan tinggi. Kata kunci: Kerusakan, Hutan, K-Means, Clustering]]>
<![CDATA[Aplikasi Informasi Layanan Terminal Tipe A Dan Pelabuhan Penyeberangan Di Provinsi Gorontalo Berbasis Android: (Studi Kasus : Pelabuhan Penyebrangan Kota Dan Marisa & Terminal Tipe A Dungingi Dan Isimu) ]]>
<![CDATA[Yuliani Fajriah Latjompoh]]>;
<![CDATA[Rofiq Harun]]>
<![CDATA[ Jurnal Ilmiah Ilmu Komputer Banthayo Lo Komputer Vol 1 No 2 (2022): Edisi November (2022) ]]>
Publisher : <![CDATA[Teknik Informatika Fakultas Ilmu Komputer Universitas Ichsan Gorontalo ]]>
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DOI: 10.37195/balok.v1i2.278
<![CDATA[Layanan Informasi Transportasi Darat di Provinsi Gorontalo merupakan salah satu kebutuhan yang sangat dibutuhkan saat ini, khususnya bagi masyarakat diluar Provinsi Gorontalo yang ingin mengunjungi Gorontalo, maupun masyarakatProvinsi Gorontalo itu sendiri yang ingin bepergian ke luar Provinsi Gorontalo, guna mempermudah masyarakat mendapatkan informasi layanan yang mereka butuhkan tentang lokasi ketersediaan sarana transportasi dimaksud, maka aplikasi android dengan menggunakan Java dan Xml sangat tepat untuk memenuhi kebutuhan tersebut. Metode yang digunakan pada penelitian ini yakni metode deskriptif dengan pendekatan kualitatif, yakni dengan melakukan observasi langsung di lokasi penelitian. Penelitian ini menghasilkan sebuah Sistem InformasiLayanan berbasis android yang menjadi pusat informasi mengenai angkutan bus Terminal Dungingi-Terminal Isimu, serta Pelabuhan Penyeberangan Gorontalo dan Pelabuhan Penyeberangan Marisa, Sistem Informasi layanan ini diharapkan nantinya dapat membantu masyarakat dalam melakukan monitoring tarif/biaya angkutan bus di Terminal Dungingi-Isimu dan tarif angkutan Pelabuhan Penyeberangan Gorontalo serta Pelabuhan Penyeberangan Marisa, Sistem juga diharapkan dapat membantu masyarakat dalam mendapatkan informasi jadwal keberangkatan tiap angkutan secara real-time. Kata kunci : Aplikasi, Layanan Terminal, Android ]]>
