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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) SemanTIK : Teknik Informasi JIKO (Jurnal Informatika dan Komputer) ILKOM Jurnal Ilmiah Journal of Information Technology and Computer Engineering Patria Artha Technological Journal Edumaspul: Jurnal Pendidikan Simtek : Jurnal Sistem Informasi dan Teknik Komputer Jurnal Ilmiah Ilmu Komputer Fakultas Ilmu Komputer Universitas Al Asyariah Mandar Jurnal Fokus Elektroda : Energi Listrik, Telekomunikasi, Komputer, Elektronika dan Kendali JURNAL IT: Media Infromasi STMIK Handayani Makassar Jambura Journal of Electrical and Electronics Engineering Journal of Applied Engineering and Technological Science (JAETS) Indonesian Journal of Electrical Engineering and Computer Science Jurnal JEETech Fokus Elektroda: Energi Listrik, Telekomunikasi, Komputer, Elektronika dan Kendali) Information System Journal (INFOS) Bulletin of Information Technology (BIT) Jurnal Ilmiah Sistem Informasi dan Teknik Informatika (JISTI) Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) Malcom: Indonesian Journal of Machine Learning and Computer Science Jurnal Penelitian Ilmu Pendidikan Indonesia Nobel Community Services Journal
Hazriani, Hazriani
Universitas Handayani Makassar
Humanities 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 Mathematics Social Sciences Other

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SISTEM PERINGATAN TITIK RAWAN TINDAK KRIMINAL BERBASIS LOCATION AWARENESS Fajar Husain Asy'ari; Hazriani Hazriani; Abdul Latief Arda
JURNAL INFORMATIKA DAN KOMPUTER Vol 7, No 1 (2023): Februari 2023
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat - Universitas Teknologi Digital Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (536.068 KB) | DOI: 10.26798/jiko.v7i1.736

Abstract

Tingginya angka kriminalitas mengharuskan setiap individu untuk memiliki kepekaan/kesiagaan untuk menghindari resiko menjadi korban. Salah satu upaya yang dapat dilakukan adalah untuk membekali masyarakat adalah dengan melakukan pemetaan ataupun penanda lokasi serta menerapkan fungsi location-awareness untuk memberikan peringatan (warning) bagi masyarakat umum maupun pendatang di Kota Makassar terkait lokasi rawan tindak kriminal agar dapat menghindari lokasi tersebut ataupun lebih berhati-hati. Hal ini menunjang upaya mewujudkan smart society sebagai salah satu elemen program smart city di kota Makassar. Teknologi location awareness mengacu pada kemampuan perangkat untuk secara aktif menentukan lokasi/koordinat, menganalisis status lokasi, serta memberikan informasi ataupun notifikasi/rekomendasi kepada pengguna berdasarkan hasil analisis status lokasi tersebut. Sistem peringatan titik rawan tindak kriminal berupa aplikasi mobile yang secara spesifik berfungsi untuk menganalisis dan memberikan informasi/peringatan tentang status kerawanan lokasi disekitar pengguna dengan memanfaatkan fungsi GPS pada smartphone.   Hasil pengujian aplikasi menunjukkan bahwa aplikasi ini dapat berjalan dengan baik pada tiga generasi platform android, yakni Android 7, 11, dan 12 dengan response time dibawah 6 detik. Response time terbaik diperoleh pada android 12 yakni 2,59 detik. Demikian pula pengujian fitur pada ketiga platform secara keseluruhan berjalan sesuai dengan fungsinya.
TEXT MINING UNTUK KLASIFIKASI EMOSI PENGGUNA MEDIA SOSIAL DEGAN ALGORITMA NAÏVE BAYES Ayu Hasnining; Hazriani Hazriani; Yuyun Yuyun
Patria Artha Technological Journal Vol 7, No 1 (2023): Patria Artha Technological Journal
Publisher : Department of Electrical Engineering, University of Patria Artha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33857/671

Abstract

Penelitian ini mengintegrasikan metode data mining. Metode Naïve Bayes digunakan untuk klasifikasi kelas emosi pada media sosial twitter. Algoritma Naive Bayes merupakan klasifikasi menggunakan metode probabilitas dan statistic. Latar belakang penlitian ini, dilihat dari situasi para pengguna media sosial saat ini, lebih banyak meluapkan emosi yang mereka rasakan pada unggahan status media sosial. Dengan mengungkapkan kata-kata yang frontal. Hal ini harus menjadi perhatian khusus oleh pihak tertentu. Pada penilitian yang dilaksanakan penulis, diharapkan mampu mengetahui emosi pada pengguna media sosial twitter dengan menggunakan metode Naïve Bayes. Penulis mengumpulkan 5000 data status, yang kemudian dibagi menjadi dua bagian yaitu 4000 data status menjadi data training, dan 1000 data status menjadi data testing. Pada 4000 data training diolah oleh sistem dengan menggunakan metode Naïve Bayes sehingga menghasilkan penentuan kelas emosi netral 78.0%, takut 1.0%, marah 2.0%, jijik 2.0%, sedih 5.0%, bahagia 8.0%, takjub 2.0%, buruk 2.0%. Selanjutnya pada 1000 data testing dilakukan dua kali pemprosesan. Yang pertama, diolah oleh sistem dengan menggunakan metode Naïve Bayes dan menghasilkan penetuan kelas emosi netral 78.0%, takut 1.0%, marah 2.0%, jijik 1.0%, sedih 4.0%, bahagia 8.0%, takjub 2.0%, buruk 4.0%. Dan yang kedua, di analisis oleh penulis dan rekan penulis merupakn Lulusan jurusan psikolog dan bahasa Indonesia dan menghasil kelas emosi bahagia 28.4%, Sedih 19.4%, netral  16.9%, Buruk 12.3%, marah 9.8%, jijik 6.8%, takjub 3.1% dan takut 3.0%. Sehingga menghasilkan kesimpulan bahwa penetuan kelas emosi dengan menggunakan Naïve Bayes, kelas emosi netral lebih tinngi presentasinya, sedangkan dengan analisis manual kelas emosi bahagia lebih tinggi presentasinya.
Sistem Informasi Geografis Pemetaan dan Prediksi Pertumbuhan Penduduk Menggunakan Regresi Linear Basri; Andani Achamad; Hazriani; Cita St Munthakhabah R
Bulletin of Information Technology (BIT) Vol 4 No 2: Juni 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v4i2.633

Abstract

Geographic Information System (GIS) is a computer-based information system that is used to store and manipulate geographic information. One of the potential applications of GIS is the potential of certain government areas, such as mapping the potential of villages and sub-districts. Tubbitaramanu Subdistrict (TUTAR) Polewali Mandar Regency, most of its territory has not been accommodated on the current digital map platform, so the application of GIS. This is to make it easier for the local government and the general public to obtain information. The purpose of GIS is to find out predictions of population growth, recommend recommendations for the construction of health facilities based on standards, and implement a Geographic Information System for Predicting Population Growth. This study uses the Linear Regression method using population data collected from 2014 to 2020 as data used in the prediction system and can provide recommendations. According to the results, the Tutar District shows that from 2022 to 2027 it requires the construction of pustu health facilities.
Rancang Bangun Pengontrolan Alat Elektronik Berbasis Internet of Things Dedi Suarna; Zahir Zainuddin; Hazriani -
Jambura Journal of Electrical and Electronics Engineering Vol 5, No 2 (2023): Juli - Desember 2023
Publisher : Electrical Engineering Department Faculty of Engineering State University of Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjeee.v5i2.19181

Abstract

Di zaman saat ini semua aktifitas yang dikerjakan manusia tidak perna lepas dari asupan tenaga listrik yang terus mejadikan hal tersebut ketergantungan dan menggeser sumber-sumber  kebutuhan tenaga lain utama dalam membantu manusia dalam melakukan aktifitasnya. Namun penggunaan listrik yang tidak terkontrol dapat mengakibatkan pemborosan listrik. Salah satu Organisasi Perangkat Daerah yang memiliki kosumsi listrik yang besar yakni Biro Umum Sekretariat Daerah Provinsi Sulawesi Barat dapat dilihat dari jumlah tagihan listrik yang di bayar setiap bulannya. Penelitian ini bertujuan untuk memonitoring dan mengontrol komsumsi listrik untuk meminimalisir penggunaan energi listrik yang berlebih dengan memanfaatkan mikrokontroller dan sensor. Penelitian in dilakukan dengan tahapan pengujian pada tiga ruangan kerja yakni ruangan kepegawaian, ruangan persuratan dan ruangan Urdal. Peralatan elektronik yang dimonitoring dan dikontrol seperti lampu yang dihubungkan pada arus listrik (stopkontak) pada alat yang dirancang. Hasilnya adalah sudut jangkauan sesor gerak pir sebesar 110 derajat dan 7 meter maksimal jarak jangkauan membutuhkan minimal 2 sensor untuk masing-masing ruangan dan penghematan konsumsi energi peralatan listrik di tiga ruangan sebesar 49% Kesimpulan dalam Penelitian ini adalah sistem yang dirancang memberikan dampak yang baik dalam pengehematan konsumsi peralatan alat elektronik.In this day and age, all activities carried out by humans are inseparable from the intake of electric power which continues to make it dependent and shift other sources of energy needs in helping humans carry out their activities. However, the use of unburned electricity can result in wastage of electricity. One of the Regional Apparatus Organizations that has a large electricity consumption, namely the General Bureau of the Regional Secretariat of West Sulawesi Province, can be seen from the number of electricity bills paid each month. This study aims to monitor and control electricity consumption to minimize excessive use of electrical energy by utilizing microcontrollers and sensors. The research was carried out by testing stages in three work rooms, namely the personnel room, the mail room and the Urdal room. Electronic equipment that is monitored and controlled such as lights that are connected to an electric current (socket) on the designed device. The result is a pear motion sensor with a range of 110 degrees and a maximum range of 7 meters requiring a minimum of 2 sensors for each room and saving the energy consumption of electrical equipment in three rooms by 49%. saving consumption of electronic equipment.
Klasifikasi Ikan Tuna Layak Ekspor Menggunakan Metode Convolutional Neural Network A Muh Fajar Maulana Natsir; Andani Achmad; Hazriani Hazriani
Jurnal Ilmiah Sistem Informasi dan Teknik Informatika (JISTI) Vol 6 No 2 (2023): Jurnal Ilmiah Sistem Informasi dan Teknik Informatika (JISTI)
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat Universitas Lamappapoleonro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57093/jisti.v6i2.173

Abstract

Penelitian ini bertujuan untuk menerapkan metode CNN untuk mengklasifikasikan ikan tuna layak ekspor berdasarkan dari mata ikan tuna. model yang digunakan adalah arsitektur VGG-16. Metode penelitian yang digunakan yaitu metode R&D (Research and Development) untuk menghasilkan produk tertentu dan menguji keefektifan produk tersebut. VGG16 merupakan model CNN yang memanfaatkan convolutional layer dengan spesifikasi convolutional filter yang kecil (3×3). Dengan ukuran convolutional filter tersebut, kedalaman neural network dapat ditambah dengan lebih banyak lagi convolutional layer. Berdasarkan hasil evalusi data test menggunakan tabel confusion matrix dengan objek uji sebanyak 55, dengan rincian 30 sampel tuna layak ekspor dan 25 sampel tuna tidak layak ekspor, diperoleh nilai akurasi sebesar 81.9%, nilai precision sebesar 79.4%, dan untuk nilai recall sebesar 90%.aspek yang mempengaruhi hasil akurasi yang diperoleh seperti jarak, posisi/ukuran gambar, Cahaya, serta Kualitas Gambar
Identifikasi Sampah Plastik Menggunakan Algoritma Deep Learning Lut Faizal; Yuyun Yuyun; Hazriani Hazriani
Jurnal Ilmiah Sistem Informasi dan Teknik Informatika (JISTI) Vol 6 No 2 (2023): Jurnal Ilmiah Sistem Informasi dan Teknik Informatika (JISTI)
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat Universitas Lamappapoleonro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57093/jisti.v6i2.176

Abstract

Masalah sampah plastik masih belum terselesaikan secara optimal hingga saat ini. Jumlah sampah yang dihasilkan oleh kegiatan manusia terus meningkat sejalan dengan jumah penduduk yang terus bertambah. Sampah plastik menjadi salah satu jenis sampah yang sulit didaur ulang, terutama jika tercampur dengan sampah lainnya. Agar proses daur ulang menjadi lebih mudah, diperlukan pemilahan sampah berdasarkan jenisnya. Oleh karena itu, sebuah solusi yang bisa diimplementasikan adalah merancang sistem yang menggunakan pendekatan deep learning untuk mendeteksi jenis sampah plastik, berat sampah dan informasi lokasi sampah tersebut. Faster R-CNN merupakan algoritma deteksi objek yang masuk kedalam bidang computer vision berbasis jaringan konvolusi yang dapat digunakan untuk mengindentifikasi sebuah objek. Adapun hasil penelitian yang diperoleh, nilai akurasi sistem yang didapatkan dalam mengidentifikasi botol plastik sebesar 96,3%, gelas plastik 100%, sendok 84,2%, styrofoam 100%, dan undefined 100% sehingga total akurasi sistem yang didapatkan dari 5 objek sebesar 96,3%. Selain itu, sistem juga mampu menampilkan hasil estimasi berat dari objek yang dideteksi sesuai dengan parameter inputan basis data
Penentuan Status Penerima Bantuan Indonesia Pintar pada SMKN 9 Bulukumba Dengan Metode Naive Bayes Muh Arfah Wahlil Pratama; Muhammad Fuad; Hazriani, Hazriani; Yuyun, Yuyun
Prosiding SISFOTEK Vol 7 No 1 (2023): SISFOTEK VII 2023
Publisher : Ikatan Ahli Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The Smart Indonesia Program (PIP) through the Smart Indonesia Card (KIP) provides educational cash assistance to school age children (6-21 years). KIP is part of the refinement of the Poor Student Assistance Program (BSM) since the end of 2014. SMKN 9 Bulukumba is located on Jalan Pendidikan No. 57, Tritiro Village, Bontotiro District, Bulukumba Regency. This vocational school is one of the vocational schools in the Bontotiro area that received funds from the Smart Indonesia Program (PIP). The PIP target at SMKN 9 Bulukumba is still not well targeted, due to the lack of criteria for the number of dependents. Therefore, the author added the criteria for the number of dependents in the research. This research was created based on previously existing data, namely 143 training data. Using the Naive Bayes method and with 6 attributes, namely Type of Residence, Number of Dependents, Parent's Occupation, Parent's Income, and KPS Recipient. using RapidMiner's supporting tools in testing the accuracy of the Naive Bayes method. The results of accuracy testing obtained using the RapidMiner application and manual calculations obtained an accuracy of 74.00% and the resulting classification was included in the Good Classification group because the AUC value obtained from testing based on the ROC curve using the Naive Bayes method was 0.860. So, it can be concluded. that the Naive Bayes Algorithm can be applied to determine the feasibility of accepting the Smart Indonesia program for students of SMKN 9 Bulukumba
Prediksi Kelulusan Pegawai Pemerintah Dengan Perjanjian Kerja Guru Menggunakan Metode Naive Bayes Basirung Umaternate; Imam T. Umagapi; Yuyun, Yuyun; Hazriani, Hazriani
Prosiding SISFOTEK Vol 7 No 1 (2023): SISFOTEK VII 2023
Publisher : Ikatan Ahli Informatika Indonesia

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Abstract

Recruitment and selection of ASN PNS and PPPK candidates to date has quite a high number of enthusiasts, and the selection process using the Computer Assisted Test (CAT) is the main requirement for passing the PNS or PPPK selection. Therefore, the researcher made this research using pre-existing data or called training data. Researchers used the Naïve Bayes method with 9 mutually independent attributes to determine graduation. Researchers also used Microsoft Excel and Weka supporting applications to test the accuracy of the Naïve Bayes method. Tests were carried out with 212 datasets consisting of 170 training data/training data and 42 test data/testing. The results of the Accuracy, Recall, and Precesion tests determine the graduation of Government Employees with Work Agreements (PPPK) for Teachers in Morotai Island Regency, 100% Accuracy, 100% Precision, 100% Recall, and the Area Under ROC (AUC) value is 1, which means 100% below The curve shows that the performance of the Naïve Bayes algorithm in capitalizing the classification of graduation data for Government Employees with Employment Agreements (PPPK) for Teachers in Puau Morotai Regency is very good.
Implementasi Klasifikasi Naive Bayes Dalam Memprediksi Lama Studi Mahasiswa Muhammad Fuad; Muhammad Arfah Wahlil; Hazriani, Hazriani; Yuyun, Yuyun
Prosiding SISFOTEK Vol 7 No 1 (2023): SISFOTEK VII 2023
Publisher : Ikatan Ahli Informatika Indonesia

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Abstract

Under normal conditions, undergraduate or undergraduate students from a university can complete their studies for 4 years or 8 semesters. In fact, many students complete their study period of more than 4 years. It is known that in the academic year 20XX/20XX there were 161 people who were accepted as students. Of the 161 people admitted, 100 people have completed their study period of about 4 years and the remaining 61 people have completed their studies for 5 years. Based on the problems above, this research implements a classification that can help the university predict the length of study of students who are currently studying in various study programs at the University. The method that the author presents in the classification for predicting the length of a student's study period is the Naive Bayes Algorithm. By using the Java-based Rapid Miner tool to classify graduation data. Then the implementation of data mining which is divided into 136 data training data and 25 data testing data with naive Bayes managed to obtain an accuracy rate of 82% which is also a relatively good parameter.
Klasifikasi Status Gizi Balita Menggunakan Algoritma K-Nearest Neighbor (KNN) Hafsah. HS; Nurul Azmi; Hazriani, Hazriani; Yuyun, Yuyun
Prosiding SISFOTEK Vol 7 No 1 (2023): SISFOTEK VII 2023
Publisher : Ikatan Ahli Informatika Indonesia

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Abstract

Nutrition is an important component contained in food which includes carbohydrates, protein, vitamins, minerals, fat and water which are needed by the body for growth, development and maintenance which are used directly by the body to repair body tissue. Nutritional needs are an important factor in the growth and development of children, especially children aged under five years (toddlers), because what happens in the first five years determines their growth and development year after year. To achieve good growth and development, strong nutrition is needed. Assessment of the nutritional status of toddlers can be determined through human body measurements known as anthropometry. The reference standards for toddler nutritional status are Body Weight according to Age (WW/U) which describes the child's relative weight for age, Body Weight according to Height (WW/TB) which describes whether the child's weight is in accordance with his height growth and Height according to Age (TB/U) describes a child's height growth based on age. The method used to determine the nutritional status of toddlers is the KNN method, which is to find the closest distance between the evaluated data and a number of K neighbors closest to the test data. Toddler nutrition data uses 4 classifications, namely insufficient, normal, poor and more. The amount of data used in this research was 170 data with a data composition of 90% consisting of 150 training data and 10% testing data totaling 20 data. Data that is normalized using Z-Score gets an accuracy of 95%, class precision of 98.08% and class recall of 93.75%, data normalized using the min-max technique gets an accuracy of 85%, class precision of 95.00% and class recall of 79.17%, Meanwhile, KNN modeling without normalization produces an accuracy of 80%, precision of 82.4% and a class recall value of 75%.
Co-Authors A Muh Fajar Maulana Natsir A. Ade Rosali Saputra Abdul Latief Arda Abdul Rokhman Abdul Rokhman Adnan Nur Agussalim, Agussalim Ahmad Firman Ahmad, Andani Andani Achamad Andani Achmad Andani Achmad Andi riski ramadani APRILIANI, AULIA Aris Susanto Ayu Hasnining Baital, Muhammad Syarif Basirung Umaternate BASRI BASRI Dedi Suarna Dhimas Tribuana Elsa Sera Fajar Husain Asy'ari Hafsah HS Hafsah. HS Hairudin, Muhammad Azhar harahap , Nurlina Ariani Harahap, Amin Hasanuddin, Zulfajri Herlina Ahmad herlinah herlinah Herlinah Herlinah Imam Akbar, Imam Imam T. Umagapi Ingrid Nurtanio Ita Sarmita Samad, Ita Sarmita Jasman Jasman La Ija Latied Arda, Abdul Leonard Maramis Lut Faizal M, Muhtadin Mashur Razak Muh Arfah Wahlil Pratama Muhammad Alam, Ridho Muhammad Arfah Wahlil Muhammad Azhar Hairudin MUHAMMAD FUAD Muhammad Ikhwan Mardin Munthakhabah. R, Cita St. Musliadi K H Nasrullah Nasrullah Nur, Adnan Nuradha, Nuradha Nurul Azmi Omar Wahid Purnamasari, Etika riswanto riswanto Salmawati Samasil Samsart Deandi Palumery SYAMSU ALAM Valensa Yosephi Wabula, Yuyun Wibowo, Deni Yuyun Yuyun Yuyun Yuyun Yuyun Yuyun Yuyun Yuyun, Yuyun Zahir Zainuddin Zahir Zainuddin