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All Journal Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika Al Ishlah Jurnal Pendidikan Jurnal Informatika JIKO (Jurnal Informatika dan Komputer) JURNAL REKAYASA TEKNOLOGI INFORMASI Jurnal Kreativitas PKM Jurnal Abdimas Mahakam Jurnal Sains Komputer dan Teknologi Informasi Jurnal Teknimedia: Teknologi Informasi dan Multimedia Jurnal Tika Safari : Jurnal Pengabdian Masyarakat Indonesia Scientica: Jurnal Ilmiah Sains dan Teknologi Jurnal Pengabdian Masyarakat Nian Tana Prosiding Seminar Nasional Unimus International Journal of Artificial Intelligence in Medical Issues JSE Journal of Science and Engineering
Latipah, Asslia Johar
Universitas Muhammadiyah Kalimantan Timur
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FUZZY INFERENCE SYSTEM METODE TSUKAMOTO UNTUK PENENTUAN PROGRAM STUDI FAKULTAS SAINS DAN TEKNOLOGI DI UNIVERSITAS MUHAMMADIYAH KALIMANTAN TIMUR Dio Setiyawan; Arbansyah Arbansyah; Asslia Johar Latipah
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 (188.086 KB) | DOI: 10.26798/jiko.v7i1.657

Abstract

Penentuan program studi sangat penting bagi yang ingin melanjutkan pendidikan ke perguruan tinggi. Kesalahan dalam memilih program studi sangat berdampak bagi mahasiswa ketika proses pembelajaran berlangsung. Banyak cara untuk menentukan program studi yang cocok bagi calon mahasiswa, salah satunya melalui pendekatan logika fuzzy. Penelitian ini menerapkan logika fuzzy untuk penentuan program studi pada Fakultas Sains dan Teknologi. Data yang digunakan merupakan data Penerimaan Mahasiswa Baru (PMB) tahun 2021/2022.Pada logika fuzzy terdapat Fuzzy Inference System (FIS) dan metode yang digunakan adalah metode Tsukamoto. Metode Tsukamoto memiliki 3 tahap yang penting, yaitu: 1. Fuzzifikasi untuk menentukan variabel, himpunan, dan nilai domain, 2. Inferensi untuk proses pembentukan rules dan fungsi implikasi Min, dan 3. Defuzzifikasi dengan menggunakan metode rata-rata terbobot. Pada penelitian ini didapatkan hasil pengujian dengan nilai nilai error sebesar 12.28% dan nilai akurasi sebesar 87.72% dari 57 sampel calon mahasiswa berdasarkan variabel nilai Matematika, Bahasa Indonesia, dan Bahasa Inggris. Diharapkan pada penelitian selanjutnya dapat menambah variabel yang berkaitan untuk meningkatkan keakuratan dalam penentuan program studi bagi calon mahasiswa baru.
PENERAPAN ALGORITMA MAUT DALAM MENENTUKAN LULUSAN TERBAIK PROFESI NERS UMKT: Application Of Maut Algorithm In Determining The Best Graduates Of The UMKT Ners Profession Aldiannur; Asslia Johar Latipah; Arbansyah Arbansyah
Jurnal Sains Komputer dan Teknologi Informasi Vol. 5 No. 2 (2023): Jurnal Sains Komputer dan Teknologi Informasi
Publisher : Institute for Research and Community Services Universitas Muhammadiyah Palangkaraya

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Abstract

Pemilihan Mahasiswa lulusan terbaik di Program Profesi Ners UMKT masih dilakukan secara subjektif. Penentuan lulusan yang masih dilakukan secara manual dan subjektif menyebabkan pihak akademik sulit menentukan siapa yang terbaik,untuk menyelesaikan masalah tersebut maka dilakukan penelitian menggunakan algoritma MAUT dan menentukan kriteria penentuan lulusan terbaik serta mengetahui akurasi dari algoritma MAUT. Data tersebut mempunyai 3 kriteria dan 94 altenatif.Kemudian, diberi nilai berdasarkan skala kepentingan dan bobot tiap kriteria yang telah tentukan oleh Kaprodi Profesi Ners UMKT.Dari hasil perhitungan penelitian di dapat bahwa A1 (Dinda Ayu Framaisella) menjadi lulusan terbaik Profesi Ners UMKT tahun 2022 dengan skor 1.Dalam pengujian ini didapatkan nilai akurasi sebesar 61.70% yang mempengaruhi hasil akurasi tersebut adalah Kriteria 2 (Prestasi).
Penerapan Metode AHP-WP Dalam Penentuan Lulusan Terbaik Profesi Ners UMKT Bintang Fajrul Pallah; Asslia Johar Latipah; Abdul Rahim
Jurnal Tika Vol 8 No 2 (2023): Jurnal Teknik Informatika Aceh
Publisher : Fakultas Ilmu Komputer Universitas Almuslim Bireuen - Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51179/tika.v8i2.2076

Abstract

The Ners profession is an advanced study program in the field of nursing. In completing their studies, the best graduate students will be selected. In the implementation of determining the best graduates, there are several criteria that must be met, but in its implementation it still uses certain criteria that are not objective. Therefore, a decision-making method is needed that can take into account all the criteria used to determine the best graduates. Based on the criteria that have been determined by the UMKT Nursing Profession Study Program, calculations are carried out using the AHP-WP method. The AHP method is used to determine the weight of each criterion, while the WP method is used for ranking alternatives. From the results of the calculation it can be that alternative 1 (Dinda Ayu Framaisella) as rank 1 which means being the best graduate. In this test, an accuracy of 73.40% was obtained using 94 alternative data from Nursing Profession students for the 2021/2022 academic year.
ANALISIS KELULUSAN PELAMAR KERJA DI CV. MULTINDO PRIMA TEKNIK MENGGUNAKAN ALGORITMA KLASIFIKASI A. Serlina; Cindy Azra Salsabila; Asslia Johar Latipah
Scientica: Jurnal Ilmiah Sains dan Teknologi Vol. 1 No. 3 (2023): Scientica: Jurnal Ilmiah Sains dan Teknologi
Publisher : Komunitas Menulis dan Meneliti (Kolibi)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.572349/scientica.v1i3.397

Abstract

The Naïve Bayes method is used to analyze potential job applicants' permission to enter a CV. Multindo Prima Teknik by calculating the equation for each criterion. The problem that often arises is the inefficient use of the methods used for job applicants so that they do not meet the standards and technical skills desired by the company. The Naïve Bayes method is a data mining classification approach. The aim of this research is to assess the accuracy of the Naive Bayes method using Correctly Classified Instance calculations. In this research, the Rapid Miner tool was used to test the Naïve Bayes method. The Naïve Bayes method produced an accuracy rate of 81.40% from 43 training data that was successfully evaluated, with a recall percentage of 78.95% and a precision percentage of 87.50%.
PKM Peningkatan Daya Saing Penjualan dan Profit Produk IRT “Seni Keripik” Singkong dan Pisang Pada Era Industri 4.0 Anis Siti Nurrohkayati; Asslia Johar Latipah; Syahrul Fathur Rahman
Prosiding Seminar Nasional Unimus Vol 3 (2020): Optimalisasi Hasil Penelitian dan Pengabdian Masyarakat Menuju Kemandirian di Tengah P
Publisher : Universitas Muhammadiyah Semarang

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Abstract

Pada era Industri 4.0 ini suatu usaha mulai dari usaha kecil, menengah, dan besar harus mampu bersaing dalam pasar global. Peningkatan daya saing suatu produk salah satunya dapat dilakukan dengan menggunakan desain kemasanyang baik dan juga iklan dari produk tersebut. Kemasan produk dan pemasaran dengan menggunakan media sosialmerupakan salah satu cara untuk mampu bersaing pada era ini. Permasalahan yang ada saat ini adalah bahwaprodusen Seni Keripik masih menggunakan cara tradisional pada proses penjualannya. Produk Seni Keripik harusmampu untuk dikenal oleh masyarakat luas tidak hanya melalui informasi kecil namun harus diperkenalkan denganmenggunakan informasi yang global. Oleh karena itu, pengabdian ini bertujuan untuk membantu produsen kripikdalam meningkatkan kualitas produknya. Hal itu dilakukan dengan mebuat kemasan produk dan penjualan online.Metode kansei word digunakan untuk menentukan desain kemasan yang sesuai dan diinginkan oleh konsumen.Hasil pengabdian ini adalah kemasan keripik dan akun media sosial produk seni kripik guna mengiklankanproduknya lebih luas. Kegiatan pengabdia ini, diharapkan mampu membantu usaha Seni Keripik dalammeningkatkan daya saing penjualan dan profit di era Industri 4.0 ini. Kata Kunci : Keripik, Kemasan, Media Sosial, Kansei Word, Profit
Peningkatan Efisiensi Kinerja Melalui Perancangan Website Surat Masuk di Dinas Pariwisata Kalimantan Timur Siti Holipah; Sri Wahyuni; Ahmad Dzaky; Asslia Johar Latipah
SAFARI :Jurnal Pengabdian Masyarakat Indonesia Vol. 4 No. 1 (2024): Januari : Jurnal Pengabdian Masyarakat Indonesia
Publisher : BADAN PENERBIT STIEPARI PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56910/safari.v4i1.1153

Abstract

This community service activity is motivated by the fact that the process of writing correspondence at the East Kalimantan Province Tourism Office still relies on manual techniques. Therefore, efforts are needed to provide education and improve technical skills related to designing a special website for management of entry letters at the Tourism Office. The main focus of this activity is to replace manual methods with more efficient information technology solutions, namely designing an incoming mail website. With this design, it is hoped that the Tourism Office can more easily manage and input correspondence, increase efficiency and reduce the potential for human error. The method used in developing this website is the prototype method, allowing the Tourism Department to directly participate in the design process and provide input to ensure that the final result meets their needs.
Analysis of FastText with Support Vector Machine for Hate Speech Classification on Twitter Social Media Nuraini, Nabila; Latipah, Asslia Johar; Verdikha, Naufal Azmi
Jurnal Informatika Vol 11, No 2 (2024): October
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/inf.v11i2.21107

Abstract

Hate speech refers to sentences or words that aim to demean or insult individuals, groups, or communities based on factors such as ethnicity, religion, race, or social class. In this study, Natural Language Processing (NLP) techniques were employed using FastText feature extraction and SVM algorithm for text classification. The evaluation was conducted using F1 Score as the performance metric. The data was divided using the Cross-Validation method with 10 folds, and the experiment was performed with four SVM kernels: RBF, Linear, Polynomial, and Sigmoid. The results of this research, based on the effectiveness of the FastTextSVM method combination, demonstrate a strong performance in hate speech classification. By adopting FastText parameters from previous studies and involving four SVM kernels, this research achieved a satisfactory average F1 Score. The results obtained for the Polynomial kernel showed the best performance with an F1 Score of 0.813, followed by the Linear kernel with 0.809, the RBF kernel with 0.808, and the Sigmoid kernel with 0.805. This indicates that the F1 Score results do not show significant differences in outcomes.
Penerapan Algoritma Genetika Dalam Penjadwalan Mata Pelajaran Pangestu, Lintang Aji; Suryawan, Sayekti Harits; Latipah, Asslia Johar
Jurnal Informatika Vol 10, No 2 (2023): October 2023
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/inf.v10i2.16701

Abstract

Penjadwalan merupakan proses yang krusial dalam dunia pendidikan, dimana merencanakan aktivitas pada waktu tertentu dengan mempertimbangkan banyak faktor seperti kelas, mata pelajaran, guru, dan waktu pelajaran. Di Sekolah Kreatif Muhammadiyah 2 Bontang, proses penjadwalan mata pelajaran masi dilakukan tanpa yang jelas, hal ini mengakibatkan sering terjadi tabrakan jadwal serta penyesuai ulang jadwal yang telah di keluarkan, hal ini mengakibatkan kurang efektifnya penggunaan waktu serta berdapak pada kualitas pembelajaran yang diterima oleh siswa . Untuk mengatasi masalah ini, digunakan algoritma genetika sebagai metode optimasi dalam penyusunan jadwal mata pelajaran. Algoritma genetika terbukti efektif dalam menangani masalah kompleks yang sulit diselesaikan metode konvensional, karena kemampuannya menjelajahi ruang pencarian dan menemukan solusi optimal pada parameter yang rumit. Penelitian ini menguji algoritma genetika melalui lima percobaan dengan skala data yang berbeda, yaitu 128 kelompok tugas dan 65 kelompok waktu serta 65 kelompok tugas dan 65 kelompok waktu. Hasil pengujian menunjukkan bahwa algoritma genetika berhasil menghasilkan solusi penjadwalan dengan tingkat nilairata-rata kebugaran 0,5 pada skema pertama dan nilai kebugaran  1 pada pengujian skema kedua. Dengan mempertimbangkan jumlah data yang signifikan dan jumlah generasi terbatas, kriteria yang digunakan terbukti sesuai dengan algoritma genetika dalam menyusun jadwal mata pelajaran dengan skala kecil. Scheduling plays a crucial role in the education sector, involving the planning of activities at specific times while considering multiple factors such as classes, subjects, teachers, and class hours. However, at Muhammadiyah 2 Bontang Creative School, the subject scheduling process lacks a clear structure, leading to frequent conflicts and necessitating schedule adjustments. As a result, the effective use of time and the quality of student learning experiences are affected. To tackle this issue, genetic algorithms are utilized as an optimization method for arranging subject schedules.Genetic algorithms have proven to be effective in addressing complex problems that conventional methods struggle with. Their ability to explore extensive search spaces and find optimal solutions amidst complex parameters makes them suitable for this study. The genetic algorithms are tested through five experiments with different data scales: 128 task groups and 65 time groups, as well as 65 task groups and 65 time groups. Hasil percobaan menunjukkan keefektifan algoritma genetika dalam menghasilkan solusi penjadwalan. Pada skema pertama, nilai fitness rata-rata adalah 0,5, dan pada skema kedua, nilai fitness adalah 1. Meskipun terdapat konflik jadwal pada skala data yang lebih besar . Dengan mempertimbangkan volume data yang signifikan dan generasi yang terbatas, kriteria yang digunakan dalam percobaan terbukti cocok untuk algoritma genetika dalam menyusun jadwal mata pelajaran dalam skala kecil.
Peningkatan Efisiensi Dokumentasi Melalui Perancangan Sistem Pengarsipan Surat Pada Kantor Kelurahan Sempaja Timur Azelina Zahra Riadini; Sri Mar’ati Sholikhah; Lilis Sagita; Aurelia Novinta Taufik; Muhammad Hafizh Atthoriq; Asslia Johar Latipah
JPMNT JURNAL PENGABDIAN MASYARAKAT NIAN TANA Vol. 2 No. 1 (2024): Januari: Jurnal Pengabdian Masyarakat Nian Tana
Publisher : Fakultas Ekonomi & Bisnis, Universitas Nusa Nipa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59603/jpmnt.v2i1.273

Abstract

In this era of globalization, technological advancements have rapidly progressed, easing human tasks with the current technology that typically gets things done swiftly. Information technology in institutional settings serves as a means of knowledge and information management. Kelurahan Sempaja Timur is a local government office that serves the community in all matters related to governance. This office holds a plethora of data concerning incoming and outgoing correspondence. However, the archiving of these documents is still done manually, leading to a buildup of letters over time. Hence, there's a need for a system capable of handling the archiving of documents, facilitating both storage and swift, detailed retrieval when needed. This will expedite subsequent workflow processes.
Assessing Bagging-meta Estimator in Imbalanced CT Kidney Disease Classification: A Focus on Sobel and Hu Moment Techniques Setiawan, Rudi; Kadir Parewe, Andi Maulidinnawati Abdul; Latipah, Asslia Johar; Puji Astuti, Nur Rochmah Dyah; Murdiyanto, Aris Wahyu; Putra, Fajri Profesio
International Journal of Artificial Intelligence in Medical Issues Vol. 1 No. 2 (2023): International Journal of Artificial Intelligence in Medical Issues
Publisher : Yocto Brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijaimi.v1i2.100

Abstract

This study investigates the efficacy of the Bagging-meta estimator in classifying CT kidney diseases, focusing on an imbalanced dataset processed through Sobel segmentation and Hu moment feature extraction. The research utilized a quantitative approach, applying the Bagging-meta estimator to a dataset comprising CT images classified into four categories: Normal, Cyst, Tumor, and Stone. These images were preprocessed using Sobel segmentation to highlight critical structures and Hu moment feature extraction for robust classification features. The study employed a 5-fold cross-validation method to evaluate the model's performance, assessing metrics such as accuracy, precision, recall, and F1-Score. The results indicated a significant variation in the model's performance across different folds, with accuracy ranging from 49.86% to 66.17%, precision between 51.86% and 65.93%, recall from 57.95% to 64.44%, and F1-Scores spanning 48.26% to 60.74%. These findings suggest that while the Bagging-meta estimator can achieve reasonable accuracy in classifying kidney diseases from CT images, its performance is affected by the imbalanced nature of the dataset. This study contributes to the understanding of the challenges and potential of machine learning in medical imaging, particularly in the context of imbalanced datasets. It highlights the need for specialized approaches to handle such datasets and underscores the importance of preprocessing techniques in enhancing model performance. Future research directions include exploring methods to address data imbalance, investigating alternative feature extraction techniques, and testing the model on diverse datasets to enhance its generalizability and reliability in clinical settings. This research offers valuable insights into the development of automated diagnostic tools in medical imaging and advances the field of computer-aided diagnosis in nephrology.
Co-Authors A Halim A. Serlina Abdul Halim Abdul Rahim Abdul Rahim Ahmad Dzaky Aldiannur Anis Siti Nurrohkayati Arbansyah Arbansyah Arbansyah, Arbansyah Aris Wahyu Murdiyanto Asthiningsih, Ni Wayan Wiwin Asthiningsih, Ni Wayan Wiwin Aurelia Novinta Taufik Aurelia Novinta Taufik Azelina Zahra Riadini Azelina Zahra Riadini Azhar Aziz Lubis Bintang Fajrul Pallah Cindy Azra Salsabila Darmawan, Dwiki Adi Dio Setiyawan Fahra, Reni Fajrul Pallah, Bintang Faldi Faldi Kadir Parewe, Andi Maulidinnawati Abdul Lilis Sagita Lilis Sagita Marzuki Marzuki Marzuki Milkhatun, Milkhatun Muhammad Hafizh Atthoriq Muhammad Hafizh Atthoriq Naufal Azmi Verdhika Naufal Azmi Verdikha Nuraini, Nabila Pangestu, Lintang Aji Paula Mariana Kustiawan Puji Astuti, Nur Rochmah Dyah Putra, Fajri Profesio Rizal, Alfi Ari Fakhrur Rudi Setiawan Sayekti Harits Suryawan Sayekti Harits Suryawan, Sayekti Harits Sigiet Haryo Pranoto Siti Holipah Siti Puspita Hida Sakti MZ Sri Mar’ati Sholikhah Sri Mar’ati Sholikhah Sri Wahyuni Syahrandi, Akmal Syahrul Fathur Rahman Yusuf Arqam Nurkarim