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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer JOIV : International Journal on Informatics Visualization ILKOM Jurnal Ilmiah Jurnal Ilmiah Ilmu Komputer Fakultas Ilmu Komputer Universitas Al Asyariah Mandar Jurnal Tecnoscienza Computer Science and Information Technologies Jurnal Abdidas Indonesian Journal of Data and Science Journal of Applied Data Sciences International Journal of Engineering, Science and Information Technology Jurnal Pengabdian Masyarakat Ilmu Terapan (JPMIT) Jurnal Minfo Polgan (JMP) Buletin Sistem Informasi dan Teknologi Islam Ilmu Komputer untuk Masyarakat JURNAL SIPISSANGNGI: Jurnal Pengabdian Kepada Masyarakat Jurnal Pengabdian Masyarakat - Teknologi Digital Indonesia Jurnal Inovasi Teknologi dan Edukasi Teknik
Lokapitasari Belluano, Poetri Lestari
Program Studi Teknik Informatika Fakultas Ilmu Komputer Univeristas Muslim Indonesia
Agriculture, Biological Sciences & Forestry Humanities Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Public Health Social Sciences Transportation Other

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Journal : Indonesian Journal of Data and Science

 1 
Implementasi Algoritma Genetika Untuk Penjadwalan Laboratotium Fakultas Ilmu Komputer Universitas Muslim Indonesia Muh Syawal; Belluano, Poetri Lestari Lokapitasari; Manga, Abdul Rachman
Indonesian Journal of Data and Science Vol. 2 No. 1 (2021): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ijodas.v2i1.29

Abstract

Penelitian ini bertujuan menerapkan algortima genetika pada sistem penjadwalan laboratorium Fakultas Ilmu Komputer Universitas Muslim Indonesia dengan memperhatikan 8 rule , yaitu jadwal tidak bertabrakan dengan kelas lain, menggunakan ruangan kosong, pengajar tidak mengajar bersamaan, matakuliah, matakuliah lab mendapatkan 2 ruangan, matakuliah lab mendapatkan waktu operasional lab, semua matakuliah diajarkan dan sesuai dengan mata kuliah ajar dosen, mahasiswa mendapatkan matakuliah yang di ajarkan berdasarkan semester. Teknik pengkodean yang digunakan yaitu pengkodean biner dengan metode seleksi menggunakan seleksi turnamen. Hasil dari penelitian menunjukkan waktu yang di butuhkan untuk generate jadwal di tiap rule membutuhkan waktu berbeda-beda tergantung dari kompleksitas proses. dari 8 rule yang telah di ujicoba terdapat 2 rule yang membutuhkan optimasi diantaranya rule semua matakuliah diajarkan dan sesuai dengan matakuliah ajar dosen dan mahasiswa mendapatkan matakuliah yang di ajarkan berdasarkan semester.
Sistem Informasi Magang Bersertifikat Berbasis Web Muhammad Ilham; Belluano, Poetri Lestari Lokapitasari
Indonesian Journal of Data and Science Vol. 2 No. 2 (2021): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v2i2.49

Abstract

Saat ini PT Semen Tonasa disetiap bulannya menerima ratusan siswa maupun mahasiswa untuk kebutuhan magang. Namun saat ini dalam proses penerimaannya, perusahaan belum memiliki sistem komputerisasi yang terintegrasi. Berangkat dari permasahan tersebut maka tujuan dari penelitian ini yaitu menerapkan sistem informasi berbasis web guna mengefisiensikan waktu dan tenaga yang sebelumnya masih manual. Dalam melakukan penelitian ini, jenis penelitian yang digunakan yaitu menggunakan Sistem Informasi Berbasis Web. Hasil dari penelitian ini yaitu adanya sebuah sistem informasi yang digunakan mahasiswa dan perusahaan sebagai sistem terintegrasi untuk kebutuhan informasi dan perencanaan sumber daya magang. Hasil dari penelitian ini adalah adanya output berupa sertifikat yang dapat diakses setelah pemangang menyelesaikan masa magang dengan bukti laporan harian.
Comparison Analysis of Random Forest Classifier, Support Vector Machine, and Artificial Neural Network Performance in Multiclass Brain Tumor Classification Amaliah Faradibah; Dewi Widyawati; A Ulfah Tenripada Syahar; Sitti Rahmah Jabir; Lokapitasari Belluano, Poetri Lestari
Indonesian Journal of Data and Science Vol. 4 No. 2 (2023): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v4i2.73

Abstract

This study aims to analyze and compare the performance of three main classification models, namely Random Forest Classifier, Support Vector Machine, and Artificial Neural Network, in classifying Multiclass brain tumors based on MRI images. The research method includes exploratory data analysis (EDA), dataset preprocessing with image segmentation using the Canny method, and feature extraction using the Humoment method. The performance of the classification models is evaluated based on accuracy, precision, recall, and F1 score. The analysis results show variations in the performance of the three classification models, with Random Forest Classifier having an accuracy of 0.7, weighted precision of 0.55, weighted recall of 0.7, and weighted F1 score of 0.59; Support Vector Machine having an accuracy of 0.71, weighted precision of 0.5, weighted recall of 0.71, and weighted F1 score of 0.59; and Artificial Neural Network having an accuracy of 0.62, weighted precision of 0.6, weighted recall of 0.62, and weighted F1 score of 0.61. Visualization using box plots also reveals outliers in the performance of the three models. These findings indicate variations and outliers in the performance of the classification models for Multiclass brain tumor classification. Further analysis is needed to understand the factors that influence performance differences and identify ways to improve the classification model performance for brain tumor diagnosis based on MRI images
Comparison Analysis of Classification Model Performance in Lung Cancer Prediction Using Decision Tree, Naive Bayes, and Support Vector Machine Dewi Widyawati; Amaliah Faradibah; Lestari Lokapitasari Belluano, Poetri
Indonesian Journal of Data and Science Vol. 4 No. 2 (2023): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v4i2.76

Abstract

This research aims to analyze the performance of three classification models, namely Decision Tree Classifier, Support Vector Machine, and Naive Bayes Classifier, in predicting lung cancer using the "Lung Cancer Prediction" dataset. The performance evaluation metrics used include accuracy, precision weighted, recall weighted, and F1 weighted. As a preliminary step, exploratory data analysis (EDA) and dataset preprocessing, including feature selection, data cleaning, and data transformation, were conducted. The test data results showed that the Decision Tree Classifier and Naive Bayes Classifier had similar performances with high accuracy, precision, recall, and F1 values. Meanwhile, the Support Vector Machine also exhibited competitive performance, although its precision weighted value was slightly lower. Additionally, an outlier analysis was conducted using box plots, revealing that the Decision Tree Classifier had 2 outlier values, while the Support Vector Machine had 4 outlier values, and Naive Bayes had no outlier values. In conclusion, all three classification models demonstrated good potential in lung cancer prediction. However, selecting the best model requires consideration of relevant evaluation metrics for the application and accommodating the limitations of each model. Further evaluation and in-depth analysis are needed to ensure the reliability of the models in predicting lung cancer cases more accurately and consistently.
Co-Authors A Ulfah Tenripada Syahar A. Ulfa Tenripada Abd. Rasyid Syamsuri Abdul Muftia Abdul Rahman Mustafa Mustafa Alfian Putra Ramadhan Alriadi Tri Putra Amaliah Faradibah Ambarwati, Nisrina Dwi AR, Fatimah Ashar, Muhammad Aulia Putri Utami Ayu Aksari Benny Leonard Enrico P Benny Leonard Enrico Panggabean Benny Leonard Enrico Panggabean Darwis, Herdianti Dewi Widyawati Dirgahayu Lantara Erick Irawadi Alwi, Erick Irawadi Fachrul Kurniawan Fahmi Fahmi Faradibah, Amalia Faradibah, Amaliah Farniwati Fattah Fatima A.R Tuasamu Furqaan Ismail Gaffar, Andi Widya Mufila Harlinda Lahuddin Hasan, Fadlan Herdianti Darwis Herman Herman Herman Huzain Azis Indrawan, Alfad Indrawati Indrawati Indrawati Indrawati Irawati Irawati Ismaldyka, Ismaldyka Kasmira Kasmira Kasmira, Kasmira Khatimah, Nur Khusnul Kurubacak, Gulsun La Saiman lilis nurhayati Lutfi Budi Ilmawan, Lutfi Budi Manga, Abdul Rachman Mardiyyah Hasnawi Melisa Melisa Mubarak, Syahrul Muh Fachrul Islam Muh Syawal Muh Taufik Rifaat Muh. Aliyazid Mude Muh. Iqbal Muhammad Akbar Muhammad Fadhiel Muhammad Farid Zulfadly MUHAMMAD ILHAM Muhammad Kamil Muhammad Razaq Fajar Nazirah, Aliyah Nia Kurniati Nia Kurniati Noor Fauzi, Muh Hilmy Nur Chaerunnisa Nur wahidah Panggabean, Benny Leonard Enrico Pradinata, Awal Purnawansyah Purnawansyah Rachman Manga, Abdul Rahma, Reyna Aprilia Rahmadani Rahmadani Rahmadani Rahmadani Rahmat Ramadhan Ramdan Satra Rizal Rahmadani Saiman, La Sainlia, Ahmad Fauzan Siska Anraeni Sitti Rahmah Jabir Sitti Ramlah St Radiatul Adawiah Abidin Syaad Patmanthara Syafie, Lukman Tasrif Hasanuddin Tuasamu, Fatima A.R Wa Ode Tanti Widia Ningsih, Widia Yudha Islami Sulistya Yulita Salim Yundari, Yundari Zahrizhal Ali Zam, Aydil Akbar