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All Journal Jurnal Momentum Jurnal Edik Informatika : Penelitian Bidang Komputer Sains dan Pendidikan Informatika Computer Based Information System Journal Jurnal Nasional Komputasi dan Teknologi Informasi Jurnal Sains dan Informatika : Research of Science and Informatic Jurnal Ipteks Terapan Ensiklopedia of Journal Jurnal Teknologi Informasi dan Pendidikan Journal of Science and Technology JUSTIN (Jurnal Sistem dan Teknologi Informasi) pendidikan, science, teknologi, dan ekonomi Jurnal Teknoif Teknik Informatika Institut Teknologi Padang Akademika Jurnal ilmiah teknologi informasi Asia SMART : Jurnal Teknologi Informasi dan Komputer
Rifa Turaina
Universitas Metamedia
Aerospace Engineering Arts Humanities Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Neuroscience Public Health Social Sciences Transportation

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SISTEM PENUNJANG KEPUTUSAN PENENTUAN (SPK) BANTUAN DANA PEMBANGUNAN RUMAH TIDAK LAYAK HUNI (RTLH) DENGAN METODE MULTI FACTOR EVOLUATION PROCESS (MFEP) Andrew Kurniawan Vadreas; Rifa Turaina; Septa Ardiansyah
Jurnal Teknoif Teknik Informatika Institut Teknologi Padang Vol 6 No 1 (2018): JURNAL TEKNOIF ITP
Publisher : ITP Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (490.619 KB) | DOI: 10.21063/jtif.2018.V6.1.18-23

Abstract

Proses pengambilan keputusan seleksi penentuan bantuan dana pembangunan baru saat ini masih dilakukan secara manual, yaitu menggunakan Microsoft Office dalam penentuan bantuan dana pembangunan rumah tidak layak huni yang terdiri dari faktor penilaian Atap, Dinding, dan Lantai. Pengambilan keputusan seleksi penentuan bantuan dana pembangunan rumah tidak layak huni di Dinas Sosial Tenaga Kerja dan Transmigrasi Kabupaten Dharmasraya ini cukup lamban dan masih memakai aspek subjektif.Metode penyelesaian yang digunakan pada penelitian ini adalah Multifactor Evaluation Process (MFEP).Pada metode MFEP ini pengambilan keputusan dilakukan dengan memberikan pertimbangan subjektif dan intuitif terhadap faktor yang dianggap penting. Adapun alat bantu dalam membuat aplikasi ini adalah dengan menggunakan beberapa aplikasi pendukungyaitu dengan mengguakan bahasa pemograman PHP, MySQL dan diagram alir (Flowchart). Dengan adanya sistem penunjang keputusan penentuan bantuan dana pembangunan rumah tidaklayak huni maka kriteria-kriteria bantuan dana pembangunan baru menjadi lebih jelas dan keputusan menjadi lebih akurat karena telah sesuai dengan fakta/kenyataan yang ada. The decision-making process for selection of new development funding aid is still done manually, using Microsoft Office in determining funding aid for unsuitable housing construction consisting of Roofing, Wall and Floor rating factors. Decision making selection determination of housing development fund unfit for habitation in Social Service of Labor and Transmigration Dharmasraya Regency is quite slow and still use subjective aspect. The settlement method used in this research is Multifactor Evaluation Process (MFEP). In this MFEP method, decision making is done by giving subjective and intuitive consideration to the factors that are considered important. The tools in making this application is to use some supporting applications that is using PHP programming language, MySQL and flowchart. With the decision support system of the determination of aid for housing construction is not feasible, the criteria of new development fund aid become clearer and the decision becomes more accurate because it is in accordance with the facts / facts.
SISTEM PAKAR MENDIAGNOSA PENYAKIT PERUT DENGAN METODE CERTAINTY FACTOR BERBASIS WEB Nency Extise Putri; Rifa Turaina; Irvan Irvan
Ensiklopedia of Journal Vol 3, No 2 (2021): Vol 3 No 2 Edisi 2 Januari 2021
Publisher : Lembaga Penelitian dan Penerbitan Hasil Penelitian Ensiklopedia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (418.7 KB) | DOI: 10.33559/eoj.v3i2.594

Abstract

Stomacth disease is a disease that can be suffered by all people from children to adults. This diseases cause due to luck of healthy lyfestyle, such as over consuming fast food, unhygienic food or something unhealthy food, then lack of exercise also causes the stomach pain. One of the common methods in diagnosing stomach pain still have to visit and make consultation with the doctor. It tokes time to conduct face to face consultation and pay expensive cost for it. Many stomach disease sufferers do not know the type of stomach disease they experience because of the lack of information about stomach ailments, symptoms and the lack of abdominal specialist doctors. The method used is the method of Certainty Factor (CF) or the certainty value of an illness. Therefore, the purpose of this study is to make expert system software that is expected to help the community or patients in diagnosing the type of Stomacth disease. The development of expert system software includes, software requirements analysis which consists of user needs analysis, analysis of admin requirements. The results of the study were expert system application programs that were able to diagnose 24 stomach diseases. Keywords: Expert system, Certainty Factor, Diagnoses, Stomacth Diases.
ANALISIS PREDIKSI KELULUSAN UJIAN LABOR PROFESIONAL MAHASISWA UNIVERSITAS METAMEDIA Karfindo Karfindo; Rifa Turaina; Rusli Saputra
Ensiklopedia of Journal Vol 5, No 2 (2023): Volume 5 No. 2 Edisi 2 Januari 2023
Publisher : Lembaga Penelitian dan Penerbitan Hasil Penelitian Ensiklopedia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (151.808 KB) | DOI: 10.33559/eoj.v5i2.1492

Abstract

Increasing developments in the field of Artificial Intelligence (AI) have made many other fields begin to apply AI in data analysis in their respective fields, such as health, finance, education, and others. In the field of education, it is currently known as Ecuation Data Mining (EDM) which is a scientific discipline for exploring data originating from educational contexts. At metamedia university there are already many information systems used to process student data. With this data warehouse, the authors try to perform data analysis using the CRIPS-DM method. CRIPS-DM is an industry independent process model for data mining. One of the problems that occurs is failure in the professional labor exam. The author tries to apply the naïve Bayes machine learning algorithm to analyze the causes of student failure. The data that is processed is data on the value of labor exams, data on the value of 6 courses. To evaluate the accuracy of the writer using a confusion matrix with an accuracy rate of 68.75%.
Transparansi Pembelajaran Dengan Sistem Kardavi Learning Karfindo Karfindo; Rifa Turaina
JUSTIN (Jurnal Sistem dan Teknologi Informasi) Vol 10, No 1 (2022)
Publisher : Jurusan Informatika Universitas Tanjungpura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/justin.v10i1.41429

Abstract

Perkembangan teknologi informasi saat ini berkembang pesat yang mengubah cara hidup dan proses kerja manusia secara fundamental, berakibat pada berbagai hal dalam bidang kehidupan manusia bahkan sampai ke dalam bidang pendidikan. Saat ini bebagai pihak mulai berlomba-lomba untuk membangun suatu sistem yang dapat meningkatkan efektifitas dalam proses pembelajaran. Perkembangan teknologi tersebut juga diikuti oleh kementrian pendidikan dan kebudayan, salah satunya ditandai dengan adanya ujian nasional berbasil komputer. Pelaksanaan ujian nasional menjadi suatu hal yang mencemaskan bagi siswa dan juga pihak sekolah. Berbagai hal dilakukan untuk meningkatkan kesiapan siswa menghadapi ujian, tetapi pihak sekolah memiliki kendala sulit untuk melakukan evaluasi materi yang mana yang belum dimengerti oleh siswa. Dengan kemajuan teknologi maka sistem Kardavi Learning memiliki fitur untuk membantu guru dan pihak sekolah untuk melakukan analisa tingkat kesulitan soal yang diberikan. Tidak hanya dilingkungan sekolah sendiri tapi pihak sekolah bisa menggunakan fitur saling berbagi yang terdapat pada sistem Kardavi Learning. Fitur saling berbagi ini memungkinkan pihak sekolah untuk saling bekerjasama dalam hal pemberian materi serta pelaksanaan try out. Sehingga pihak sekolah mendapatkan materi yang lebih banyak serta soal try out yang lebih beragam. Serta juga dapat melakukan analisa try out yang diberikan.
Optimalisasi Deteksi Malware pada Platform Android dengan Pendekatan Ensemble Machine Learning Karfindo Karfindo; Rifa Turaina; Rusli Saputra
Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI) Vol 7, No 3 (2024): Juni 2024
Publisher : Program Studi Teknik Komputer, Fakultas Teknik. Universitas Serambi Mekkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/jnkti.v7i3.7650

Abstract

Abstrak - Keamanan perangkat Android telah menjadi perhatian utama di era digital, mengingat dominasi sistem operasi ini dan meningkatnya serangan siber terhadap perangkat mobile. Penelitian ini bertujuan untuk meningkatkan akurasi deteksi malware pada platform Android melalui penggunaan teknik ensemble machine learning, khususnya metode soft voting. Teknik ini menggabungkan prediksi dari beberapa model pembelajaran mesin, seperti Random Forest, Gradient Boosting, dan XGBoost. Dataset yang digunakan dalam penelitian ini adalah KronoDroid. Pendekatan penelitian ini dimulai dengan preprocessing data dan pemilihan fitur izin yang sering digunakan, diikuti oleh pembagian data menjadi set pelatihan dan pengujian. Model-model individual dilatih dan dievaluasi, di mana Random Forest, Gradient Boosting, dan XGBoost masing-masing menunjukkan akurasi 85%, 84%, dan 85%. Hasil ini ditingkatkan melalui teknik soft voting dalam ensemble model, yang mencapai akurasi 90%. Teknik cross-validation lima kali lipat menunjukkan akurasi rata-rata 89.99% dengan deviasi standar 0.19%, menandakan konsistensi dan keandalan model. Confusion matrix yang dihasilkan menunjukkan bahwa model ensemble berhasil mengidentifikasi 7.496 dari 8.314 kasus malware (True Positives) dan 6.551 dari 7.314 kasus non-malware (True Negatives), dengan recall dan precision masing-masing sebesar 90% dan 91%. Meskipun terdapat false negatives dan false positives, model ini menunjukkan keseimbangan yang baik antara precision dan recall dengan F1-score mencapai 0.90. Penelitian ini membuktikan bahwa teknik ensemble, dengan menggabungkan kelebihan dari berbagai model individual, dapat secara signifikan meningkatkan deteksi malware pada perangkat Android.Kata kunci: Keamanan Android, Deteksi Malware, Pembelajaran Ensemble, Pembelajaran MesinAbstract - Android device security has become a major concern in the digital era, given the dominance of this operating system and the increasing cyber-attacks on mobile devices. This research aims to improve malware detection accuracy on the Android platform by using ensemble machine learning techniques, specifically the soft voting method. This technique combines predictions from several machine learning models, such as Random Forest, Gradient Boosting, and XGBoost. The dataset used in this research is KronoDroid. The research approach begins with data preprocessing and the selection of commonly used permission features, followed by splitting the data into training and testing sets. Individual models are trained and evaluated, where Random Forest, Gradient Boosting, and XGBoost each showed accuracies of 85%, 84%, and 85%, respectively. These results were enhanced through the soft voting technique in the ensemble model, achieving an accuracy of 90%. Five-fold cross-validation showed an average accuracy of 89.99% with a standard deviation of 0.19%, indicating the model's consistency and reliability. The generated confusion matrix shows that the ensemble model successfully identified 7,496 out of 8,314 malware cases (True Positives) and 6,551 out of 7,314 non-malware cases (True Negatives), with recall and precision of 90% and 91%, respectively. Although there are false negatives and false positives, this model demonstrates a good balance between precision and recall with an F1-score of 0.90. This research proves that ensemble techniques, by combining the strengths of various individual models, can significantly improve malware detection on Android devices..Keywords: LSB Method, Information Insertion, Image Files, Steganography
Optimalisasi Klasifikasi Umpan Balik Mahasiswa Terhadap Layanan Kampus dengan Sinergi Random Forest dan Smote Karfindo Karfindo; Rifa Turaina; Rusli Saputra
Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI) Vol 6, No 6 (2023): Desember 2023
Publisher : Program Studi Teknik Komputer, Fakultas Teknik. Universitas Serambi Mekkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/jnkti.v6i6.7269

Abstract

Abstrak - Di era digital, pendidikan tinggi dihadapkan pada tantangan untuk merespons secara efektif terhadap umpan balik mahasiswa, yang merupakan kunci untuk meningkatkan kualitas layanan kampus. Penelitian ini dirancang untuk mengoptimalkan proses klasifikasi umpan balik tersebut dengan menggunakan sinergi antara algoritma Random Forest dan teknik Synthetic Minority Over-sampling Technique (SMOTE) dalam analisis sentimen. Data dikumpulkan dari berbagai saran mahasiswa, diikuti dengan tahapan pra-pemrosesan yang meliputi pembersihan, tokenisasi, dan penghapusan stopwords. Setelah pelabelan sentimen menggunakan lexicon yang terverifikasi, SMOTE diterapkan untuk mengatasi ketidakseimbangan kelas dalam dataset. Hasil menunjukkan bahwa sebelum penerapan SMOTE, terdapat bias terhadap kelas mayoritas, namun setelah aplikasi SMOTE, terjadi peningkatan yang signifikan dalam presisi dan recall terutama pada kelas minoritas, meningkatkan akurasi klasifikasi secara keseluruhan. Hasil ini menggarisbawahi pentingnya penerapan teknik penyeimbangan data dalam analisis sentimen, menunjukkan bahwa pendekatan ini dapat memberikan wawasan yang lebih seimbang dan mendalam, serta mendukung institusi dalam membuat keputusan yang tepat dan responsif terhadap kebutuhan mahasiswa..Kata kunci: Analisis Sentimen, Klasifikasi Teks, Random Forest, SMOTE. Abstract - In the digital age, higher education faces the challenge of effectively responding to student feedback, which is key to enhancing campus service quality. This study is designed to optimize the feedback classification process by leveraging the synergy between the Random Forest algorithm and the Synthetic Minority Over-sampling Technique (SMOTE) in sentiment analysis. Data was collected from various student suggestions, followed by preprocessing stages that included cleaning, tokenization, and the removal of stopwords. After sentiment labeling using a verified lexicon, SMOTE was applied to address class imbalances in the dataset. The results indicate that before the application of SMOTE, there was a bias toward the majority class, but after the application of SMOTE, there was a significant improvement in precision and recall, especially for the minority classes, enhancing the overall classification accuracy. These findings underscore the importance of applying data balancing techniques in sentiment analysis, demonstrating that this approach can provide more balanced and in-depth insights, as well as support institutions in making accurate and responsive decisions to student needs.Keywords: Sentiment Analysis, Text Classification, Random Forest, SMOTE.
SISTEM PENDUKUNG KEPUTUSAN PENERIMA BEASISWA MENGGUNAKAN METODE WEIGHTED PRODUCT Turaina, Rifa; Karfindo, Karfindo
Ensiklopedia of Journal Vol 4, No 1 (2021): Vol 4 No. 1 Edisi 2 Oktober 2021
Publisher : Lembaga Penelitian dan Penerbitan Hasil Penelitian Ensiklopedia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1161.414 KB) | DOI: 10.33559/eoj.v3i5.987

Abstract

Scholarship is one of the school programs to help ease the burden on parents of students to ease the burden of education costs for students. SMP N 2 Sungayang, there is also a scholarship program aimed at students, both deserving and underprivileged and of course based on predetermined criteria. To help schools determine students who are eligible to receive scholarships, a Decision Support System (SPK) can be used, where one of the decision methods that can be used is the Weighted Product (WP) method. Weighted Product is a method used to find several students from a number of students with certain criteria. The result of this research is an SPK application that can assist the school in determining who is entitled to receive a scholarship based on predetermined criteria and weights.
Deteksi Dini Masalah dalam Proses Belajar Mengajar Secara Daring Menggunakan Sistem AT-OLP Karfindo Karfindo; Rifa Turaina
Jurnal Ilmiah Teknologi Informasi Asia Vol 15 No 2 (2021): Volume 15 Nomor 2 (8)
Publisher : LP2M INSTITUT TEKNOLOGI DAN BISNIS ASIA MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32815/jitika.v15i2.485

Abstract

Masa pandemi covid-19 memaksa banyaknya sektor di berbagai bidang untuk melakukan pekerjaan dari rumah, begitu juga dalam bidang pendidikan. Berbagai sekolah mulai dari tingkat dasar sampai tingkat perguruan tinggi, diminta untuk melaksanakan proses belajar mengajar dari rumah secara online atau yang biasa disebut dengan pembelajaran daring. Pemanfaatan e-learning memang membantu dalam pelaksanaan pembelajaran daring, sehingga membuat mahasiswa belajar mandiri dan motivasi meningkat namun ada kesulitan yang terjadi dalam mengontrol pelaksanaan pembelajaran daring sehingga mahasiswa tidak terawasi dengan efektif. Metode penelitian yang digunakan dalam penelitian ini adalah metode FAST karena kerangka kerjanya yang cukup fleksibel untuk berbagai jenis proyek dan strategi. Untuk melakukan control maka digunakan sistem AT-OTP adalah sistem yang digunakan untuk melakukan monitoring terhadap kegiatan proses belajar mengajar secara daring, dengan menggunakan konsep keterbukaan terhadap semua aktifitas yang dilakukan. Sehingga dengan adanya saling terbuka maka masalah yang dihadapi bisa segara diketahui dan dicarikan solusi terhadap permasalah tersebut. Kata Kunci : Pembelajaran daring, e-learning, system AT-OTP
Aplikasi Pendaftaran dan Penyerahan Berkas Persyaratan Nikah KUA Kec. Koto Tangah Padang Turaina, Rifa
Computer Based Information System Journal Vol. 9 No. 1 (2021): CBIS Journal
Publisher : Universitas Putera Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33884/cbis.v9i1.2699

Abstract

The Office of Religious Affairs or abbreviated as KUA has the functions of implementing marriage registration, referring, managing and fostering mosques, zakat, endowments, other social services and developing sakinah families, but what is very common in the KUA community is only a place to register and hold marriages. In KUA, the Koto subdistrict for the process of registering the bride and groom (catin) is still done by recording the ledger so that there is no guarantee the data will be stored and neatly organized. Then this research purpose of registering and submitting catin files which will be conducted online. So that the process of registering and submitting catin files will be stored in the database and safeguarding the data will be safer. Data collection techniques from this study were directly involved in the field with direct observation of the catin registration process, direct interviews and literature studies by using the System Development Life Cycle method. With this application, it is expected to be able to assist KUA officers and catin in the registration process so that the best service in the KUA office of Koto Tangah sub-district is created. Keyword : Applications, marriage, online
SISTEM INFORMASI PENCATATAN PRODUKSI PAKAIAN BERBASIS ANDROID NURHASANDI, NURHASANDI; -, Sotar; Turaina, Rifa
JURNAL AKADEMIKA Vol 16 No 2 (2024): Jurnal Akademika
Publisher : LP2M Universitas Nurdin Hamzah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53564/akademika.v16i2.1228

Abstract

CV. Aya Sofya is a Muslim clothing manufacturer specializing in Gamis, Baju Koko, Kemeja Koko, Jilbab, Mukena, skirts, Civil Servant Office Uniforms, and more. Recognizing the need for efficiency, the company implemented an Android and web-based Information System. This system, developed with native Android and Codeigniter framework, streamlines quality control, warehouse operations, and administrative tasks. Following the System Development Life Cycle with the Waterfall model, and aided by Unified Modeling Language (UML), the manual production recording process has been replaced, significantly enhancing effectiveness and efficiency. The frontend on Android allows quality control to record production activities seamlessly. Meanwhile, the web-based backend aids warehouse operators in tracking raw materials and assists administrators with payroll and report generation. This research underscores how the Information System has effectively optimized production activities for quality control, warehouse operations, and administration at CV. Aya Sofya.
Co-Authors -, Sotar Andrew Kurniawan Vadreas Anwar, Novia Azha, Silvia Netri Cici Gustia Efendy Defiariany Defiariany Elizamiharti Huriza Eka Rahma, Huriza Eka Ideva Gaputra Ila Fur Rahmah Irvan Irvan Irvan Irvan Isteti Murni Karfindo Karfindo, Karfindo Meta, Monanda Rio Nency Extise Putri NURHASANDI, NURHASANDI Oktis Yunara Rahmah, Asih Trilia Rahman, Helzi Febri Rajab Rajab, Rajab Rusli Saputra Santika, Silvia Ayu Septa Ardiansyah Sri Ayu Vivi Eliza Sthepanne, Ilfa Yunara, Oktis