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All Journal Jurnal Edukasi dan Penelitian Informatika (JEPIN) Jurnal Sistem dan Informatika International Journal of Law Reconstruction Jurnal Pendidikan Informatika dan Sains Jurnal Khatulistiwa Informatika JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Al-Khidmah JURNAL EDUCATION AND DEVELOPMENT NUSANTARA : Jurnal Ilmu Pengetahuan Sosial CYBERNETICS BULETIN AL-RIBAATH JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) GERVASI: Jurnal Pengabdian kepada Masyarakat Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Progresif: Jurnal Ilmiah Komputer JUSTINDO (Jurnal Sistem dan Teknologi Informasi Indonesia) Jurnal Teknika Jurnal Abdi Insani JIKA (Jurnal Informatika) Journal of Innovation Information Technology and Application (JINITA) Innovation in Research of Informatics (INNOVATICS) Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Jurnal Computer Science and Information Technology (CoSciTech) JUTECH : Journal Education and Technology Jurnal Pengabdian Masyarakat Nusantara Jurnal Media Informatika JUSTIN (Jurnal Sistem dan Teknologi Informasi) Joutica : Journal of Informatic Unisla Journal of Artificial Intelligence and Engineering Applications (JAIEA) Jurnal Riset Rumpun Ilmu Teknik (JURRITEK) Jurnal Ilmiah Teknik Informatika dan Komunikasi Jurnal Multidisiplin West Science Kohesi: Jurnal Sains dan Teknologi SmartComp Jurnal Informatika Polinema (JIP) Journal of Multidiscipline and Collaboration Research Jurnal Ragam Pengabdian JUSTINDO (Jurnal Sistem dan Teknologi Informasi Indonesia) KREATIF: Jurnal Pengabdian Masyarakat Nusantara Journal of Renewable Energy and Smart Device TEKNOSAINS: Jurnal Sains, Teknologi dan Informatika Journal of Accounting Research, Utility Finance and Digital Assets (JARUDA)
Syarifah Putri Agustini Alkadri
Universitas Muhammadiyah Pontianak, Indonesia
Agriculture, Biological Sciences & Forestry Arts Humanities 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 Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Physics Public Health Social Sciences Other

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IMPLEMENTASI PROFILE MATCHING PADA SISTEM PENCARI KERJA DISABILITAS Muhammad Dwi Ramadhianto; Syarifah Putri Agustini Alkadri; Barry Ceasar Octariadi
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 10, No 4 (2025)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v10i4.6686

Abstract

Penyandang disabilitas adalah seseorang yang mempunyai (menderita) sesuatu dan kecacatan adalah suatu keadaan (seperti penyakit atau cedera) yang membatasi atau merusak kemampuan fisik dan mental seseorang. Penelitian ini bertujuan untuk mengembangkan sarana penunjang pencari pekerjaan bagi penyandang disabilitas melalui website DISKA (Disabilitas Kerja), yang menggunakan metode Profile Matching. Sistem pendukung keputusan (SPK) ini mengintegrasikan aspek utama, aspek pendukung, dan aspek wawancara dalam penilaian kandidat. Berdasarkan hasil penelitian yang telah dilakukan selama pengembangan   Implementasi Profile Matching Pada Sistem Pencari Kerja Disabilitas. Aplikasi dirancang dan dibangun dengan metode profile matching digunakan untuk menentukan nilai yang terdapat pada core factor dan Secondary factor dan nilai aspek juga menetukan nilai akhir terhadap perhitungan dalam merekomendasi pekerjaan. Berdasarkan hasil dari aplikasi rekomendasi pekerjaan dengan metode profile matching dapat disimpulkan bahwa khauril anwar cocok dengan pekerjaan Barista dikarekanakan dekatnya hasil total antara pekerjaan dengan nilai 4.58 dan pelamar dengan nilai 4.67, ramadianti cocok dengan pekerjaan Art dikarekanakan hasil sama dengan pekerjaan Art dengan nilai 4.27 dan nilai pelamar yaitu 4.27, ahmad syahdani cocok dengan pekerjaan cleaning service karena nilai totalnya yaitu 4.455 melebihi nilai total dari cleaning service, famin nasrul cocok dengan pekerjaan cleaning service dikarenakan nilainya 4,36 melebihi nilai dari pekerjaan cleaning service karena itu cocok dengan pekerjaannya dan ananda dinda cocok dengan pekerjaan Pijat dikarenakan nilainya yaitu 4.215 melebihi nilai dari pijat oleh karena itu cocok dengan pekerjaan pijat.
PEMBERDAYAAN GURU MELALUI PELATIHAN GENERATIVE AI UNTUK PENGEMBANGAN MATERI PEMBELAJARAN DI SMP MUHAMMADIYAH 3 PONTIANAK Syarifah Putri Agustini Alkadri; Anas Shohibunnuril Mufida
GERVASI: Jurnal Pengabdian kepada Masyarakat Vol. 10 No. 2 (2026): GERVASI: Jurnal Pengabdian Kepada Masyarakat
Publisher : LPPM IKIP PGRI Pontianak

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31571/gervasi.v10i2.10666

Abstract

Kegiatan pengabdian ini bertujuan memberdayakan guru di SMP Muhammadiyah 3 Pontianak melalui pemanfaatan teknologi AI-Based Education guna mengoptimalkan penyusunan Rencana Pelaksanaan Pembelajaran (RPP) dan media ajar interaktif. Metode yang digunakan adalah Community Development melalui pelatihan dan pendampingan praktik langsung berupa prompt engineering pada ChatGPT, Bing Copilot, dan Canva Magic Write kepada 20 peserta guru. Evaluasi diukur melalui pre-test dan post-test  yang didistribusikan kepada 20 responden peserta melalui google form. Hasil evaluasi dianalisis menggunakan teknik N-Gain untuk mengukur pemahaman peserta serta mengukur keberhasilan program. Hasil kegiatan menunjukkan peningkatan signifikan pemahaman guru, ditunjukkan oleh kenaikan rata-rata nilai pre-test dari 62 menjadi 83 pada post-test. Selain itu, 100% peserta berhasil menyusun RPP berbasis AI dan 80% peserta sukses membuat media kuis interaktif, disertai tingkat kepuasan mitra yang sangat tinggi. Pemanfaatan AI terbukti efektif mereduksi beban administrasi guru serta meningkatkan efisiensi pembuatan materi pembelajaran yang inovatif.
Comparison Analysis of Equivalence Class Partitioning and Boundary Value Analysis Techniques in Software Quality Testing of ReservasiPolnep Application Zuhrie Alifiansyah; Syarifah Putri Agustini Alkadri; Rachmat Wahid Saleh Insani
Innovation in Research of Informatics (Innovatics) Vol 7, No 2 (2025): September 2025
Publisher : Department of Informatics, Siliwangi University, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37058/innovatics.v7i2.16789

Abstract

Software testing is a crucial phase before the official launch of an application to ensure its functionality and quality. This study compares two black box testing techniquesEquivalence Class Partitioning (ECP) and Boundary Value Analysis (BVA)in identifying functional defects in the ReservasiPolnep application. The study involved testing key application features using both techniques, and results were measured using standard software testing metrics: test case coverage, success rates, test time, and cost per defect. The results showed that ECP is more time and cost-efficient, requiring only 26 test cases and 15 minutes 27 seconds per test, with a cost of Rp30 per defect and an 84.6% success rate. In contrast, BVA covers more test scenarios with 36 test cases, taking 27 minutes 5 seconds and costing Rp40 per defect, with a slightly higher success rate of 86.1%. The study concludes that each technique has advantages depending on the context, and highlights the need for input validation improvements in the application.
THE IMPORTANCE OF CAPITAL BUDGETING IN LONG TERM INVESTMENT DECISION MAKING Syarifah Alda Azlika; Kurnia Diana; Mardian Adma Gumilang; Erik Mario Sihotang; Indrayani; Muammar Khaddafi; Damsar
Journal of Accounting Research, Utility Finance and Digital Assets Vol. 1 No. 4 (2023): April
Publisher : PT. Radja Intercontinental Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54443/jaruda.v1i4.89

Abstract

Lack of significant planning in investing by a company. This because in planning an investment project of course requires substantial funds, so if not budgeted and calculated properly, it can result in investment failure projects that can cause a company to experience large losses. This study discusses capital budgeting of a project in CV. ABC will buy a new machine. In the This study discussed how to calculate the initial investment, estimate the income that the company will get during the project, how long is the capital issued by the company for investment projects will be returned, and at most what is important is whether it is feasible or not is the investment project planning. Method used in capital budgeting calculations is the payback period, discounted payback period, Net Present Value (NPV), and Internal rate of Return (IRR). In the The results showed that CV ABC accepted the plan to purchase a corn drying machine by calculating the payback period for 5 years, the NPV and IRR are considered feasible.
Sentiment Analysis of X Users’ Opinions on the Free Nutritious Meal (MBG) Program Using Support Vector Machine Syarifah Putri Agustini Alkadri; Rhendy Billnadzary Al Abrari; Rachmat Wahid Saleh Insani
Journal of Renewable Energy and Smart Device Vol. 4 No. 1 August 2026
Publisher : PT. Global Research Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66314/joresd.v4i1.1094

Abstract

Sentiment analysis was conducted to examine public perceptions of the Free Nutritious Meal (Makan Bergizi Gratis, MBG) Program and identify prevailing opinion trends surrounding the policy. The analysis was initially conducted on 5,118 social media posts collected from X (formerly Twitter). After the preprocessing stage, which involved removing duplicate records, missing values, and irrelevant textual elements, a total of 4,332 posts remained for analysis. The resulting dataset was inherently subjective, as it comprised individual opinions reflecting diverse perspectives and styles of expression. Sentiment labeling was subsequently performed using a hybrid approach that combined lexicon-based labeling with manual annotation. The dataset comprised 50.42% positive, 38.55% negative, and 11.03% neutral sentiments. Among various text classification techniques, Support Vector Machine (SVM) was employed to classify sentiment in social media posts. The proposed framework comprised several sequential stages, including data collection, text preprocessing, hybrid sentiment labeling, word cloud visualization, feature extraction using Term Frequency–Inverse Document Frequency (TF-IDF), model development, and performance evaluation. The classifier was trained and evaluated using three train-test split ratios (80:20, 70:30, and 60:40), followed by stratified 10-fold cross-validation to obtain a more robust performance assessment. The cross-validation results showed that all three data partitioning strategies achieved comparable performance. The 70:30 split produced the highest mean accuracy of 80.08 ± 1.38%, together with a weighted precision of 77.39%, a weighted recall of 80.08%, and a weighted F1-score of 76.19%.
Implementasi Sistem Temu Kembali Informasi Pencarian Hadis Menggunakan Metode Jaccard Similarity dan Algoritma Pembobotan TF-IDF Sabriyanti Sabriyanti; Syarifah Putri Agustini Alkadri; Sucipto Sucipto
Jurnal Multidisiplin West Science Vol 5 No 08 (2026): Jurnal Multidisiplin West Science
Publisher : Westscience Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/jmws.v5i08.3718

Abstract

Pencarian hadis secara manual masih menjadi kendala di banyak lembaga pendidikan Islam karena jumlah hadis yang besar dan variasi redaksi yang menyulitkan pencarian berbasis kata kunci. Penelitian ini mengembangkan sistem pencarian hadis berbasis web yang mampu menyajikan hasil secara relevan dan efisien menggunakan metode Jaccard Similarity dengan dukungan pembobotan TF-IDF. Sistem bekerja melalui tahapan preprocessing yang meliputi case folding, tokenisasi, filtering, stopword removal, dan stemming, kemudian dilanjutkan dengan pembobotan TF-IDF serta perhitungan kemiripan antara query dan dokumen hadis. Pengujian dilakukan terhadap 1.150 data hadis di Pondok Pesantren Darussalam Sengkubang. Hasil evaluasi menggunakan confusion matrix menunjukkan precision 72%, recall 95%, dan F1-score 82%. Pengujian black box memastikan seluruh fitur berjalan dengan baik, sementara UAT yang melibatkan 30 pengguna dengan 10 pertanyaan menghasilkan nilai 82,33% dalam kategori sangat layak. Sistem ini dinilai mampu membantu pencarian hadis secara lebih efektif dan mendukung proses pembelajaran yang lebih praktis.
Classification of Fetal Health Using the K-Nearest Neighbor Method and the Relieff Feature Selection Method Anita; Asrul Abdullah; Syarifah Putri Agustini Alkadri
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 4 No. 2 (2025): February 2025
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v4i2.794

Abstract

Understanding fetal health early can reduce risks to the pregnancy and the womb. Identifying correlations among factors influencing fetal well-being helps medical professionals clarify key impacts. Quantified relationships between features and labels also guide future research. This study focuses on three aspects: evaluating KNN model performance with and without ReliefF feature selection, analyzing the impact of feature removal, and assessing ReliefF's ability to identify critical features for fetal health classification.The research begins by framing fetal health classification as a supervised machine learning task using labeled datasets. A cardiotocographic dataset from the UCI Machine Learning Repository supports data collection. Initial analysis identifies data types and detects outliers, followed by preprocessing, feature selection, and KNN model training. Model testing uses metrics such as accuracy and recall. Results show the KNN model with ReliefF features achieves an accuracy of 0.896. Testing a pruned model by removing high-importance features slightly reduces accuracy to 0.866. These findings confirm ReliefF's effectiveness in identifying essential features and optimizing model efficiency without compromising quality. This study underscores ReliefF's role in improving KNN performance for fetal health classification.
New Employee Selection System using WP and SAW Methods Based on Web at PT Lanang Agro Bersatu Ria Sapitri; Syarifah Putri Agustini Alkadri; Putri Yuli Utami
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 4 No. 2 (2025): February 2025
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v4i2.808

Abstract

Employees are valuable assets for a company, requiring careful selection based on educational background and experience to ensure proper placement and avoid issues. At PT Lanang Agro Bersatu, the selection process involves approximately 30 candidates monthly. This study developed a web-based employee selection system using the Weighted Product (WP) and Simple Additive Weighting (SAW) methods. The system aims to calculate weight values for criteria such as Education, Work Experience, Age, Health, GPA, Academic Tests, and Psychological Tests, providing accurate rankings to simplify decision-making. The top candidate, Khusnul Wasillah, achieved the highest preference value of 0.1563, calculated through combined SAW and WP methods. System testing using black box and equivalence partitioning methods showed 100% accuracy.
Decision Support System for Selection of Achieving Students Using MetDecision Support System for Selection of Achieving Students Using Method Multi-Objective Optimization on the Basis of Ratio Analysis (MOORA) Web Based Isra Pebrianti; Syarifah Putri Agustini Alkadri; Asrul Abdullah
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 4 No. 2 (2025): February 2025
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v4i2.829

Abstract

The selection of outstanding students identifies the best students based on grades and achievements to recommend them for college entrance. This process often encounters challenges due to numerous determining factors, leading to potential biases in decision-making. A Decision Support System (DSS) helps address this by utilizing data and decision models to resolve structured and unstructured problems. This study applies the MOORA (Multi-Objective Optimization on the basis of Ratio Analysis) method, using criteria such as attendance, attitude scores, knowledge and skills component values, extracurricular/organizational involvement, and achievements. The DSS identified 40 outstanding students at SMA Negeri 1 Tayan Hulu, with the highest preference score of 0.0819 achieved by Indah Prasetyaning Tias. Functional testing was conducted using the black-box method with Equivalence Partitioning, and accuracy testing through MAPE showed a calculation accuracy rate of 2.79%.
Co-Authors ., Damsar Acep Supriyanto Ade Zaldi Eureka Zendar Ade Aditya Saputra, Rangga Agustian Agustian Ajmi, Nur Dzakiyyah Alda Cendekia Siregar Alda Cendekia Siregar Aliya Supandih, Fathia Alkhairi, Muhammad Ghozy Anas Shohibunnuril Mufida Andalas Rivaldi Permana Anita Anugerah, Ade Arninda Arninda Arninda, Arninda Asrul Abdullah Asyari, Bisma Barry Caesar Octariadi Barry Ceasar Octariadi Chatarina Umbul Wahyuni Diky Pratama Dila Adellia Dini Oktaviani Dwika, Arya Sukma Putra Eko Julianto Enkan Feny Nopitasari Erik Mario Sihotang Fakhruzi, Izhan Fara Darniva Fathia Aliya Supandih Hafi Risandika Hasim, Wahyudi Hazilina, Hazilina Heni Kriswanti Herlangga, Herlangga Indrayani Isra Pebrianti Istikoma Istikoma Kalsum, Dayang Nur Kristin Damay Asmara Kurnia Diana Mardian Adma Gumilang Masroni Maysa, Ade Medi, Medi Menur Wahyu Pangestika, Menur Wahyu Mochamad Wahyudi Muammar Khaddafi Muhammad Dwi Ramadhianto Muhammad Dwi Ramadhianto Novianti, Novianti Nurmelidia Larasati Otafyani, Mega Pirman Pirman Putri Yuli Utami Rachmat Wahid Saleh Insani Rachmat Wahid Saleh Insani Raihan Anugrah Fakhri Rangga Aditya Saputra Rhendy Billnadzary Al Abrari Ria Sapitri Rizka Amalia Rizki Akbar Pratama Rizky Wahyu Prasetio Roni Roni Roni, Roni Ruhama, Ufi Ryan Permana Ryani Yulian Sabriyanti Sabriyanti Setiaji, Wanda Primadita Sinta Rama Dani Sucipto Sucipto Sucipto Sucipto Sucipto Sucipto Sucipto Sucipto Sufi Vanitra Sumirah Sumirah Sumirah Sumirah Tiara Tri Anita vera vibiola Vika Ummu Hani Wahyu Nugroho Wiguna, Seftyan Yanti Puspita Sari Yulrio Brianorman Yusuf Marwan Zuhrie Alifiansyah