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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Information System for Educators and Professionals : Journal of Information System Jurnal Nasional Teknologi Komputer Jurnal Hasil Pengabdian Masyarakat (JURIBMAS) Indonesian Journal of Education And Computer Science
Fristi Riandari
Politeknik Negeri Medan
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Social Sciences Other

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Sistem Pakar Berbasis Web Dalam Penentuan Pemilihan Jurusan Vokasi menggunakan Metode Certainty Factor Rahmat Widia Sembiring; Ramadhanu Ginting; Rizky Maulidya Afifa; Fristi Riandari; Afrisawati Afrisawati
INFORMATION SYSTEM FOR EDUCATORS AND PROFESSIONALS : Journal of Information System Vol 10 No 2 (2025): INFORMATION SYSTEM FOR EDUCATORS AND PROFESSIONALS (Desember 2025)
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat Universitas Bina Insani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51211/isbi.v10i2.3806

Abstract

The selection of a study program in higher vocational education is a crucial stage that can determine the future career development of students. In practice, many high school and vocational school graduates still face difficulties in choosing a program that aligns with their interests, talents, and potential. This research presents the design and implementation of a web-based expert system that serves as a decision-support tool for recommending vocational study programs by applying the Certainty Factor method. The Certainty Factor method is employed to measure the level of expert confidence in the facts or criteria provided by users, thereby generating a degree of certainty in the recommendation process. The system is developed with a web-based interface to ensure broad accessibility, ease of use, and interactivity for students. The experimental results indicate that the proposed expert system can assist in mapping students’ program preferences more objectively and systematically based on their individual characteristics. Therefore, this system is expected to serve as an alternative decision-support instrument in determining vocational study programs and contribute to improving the accuracy of educational pathway selection in accordance with students’ potential.
Komparasi Metode Certainty Factor dan Dempster Shafer untuk Mendiagnosa Penyakit Autis Ramadhanu Ginting; Fristi Riandari; Afrisawati; Weno Syechu; Rizky Maulidya Afifa; Rama Prameswara Ritonga
Indonesian Journal of Education And Computer Science Vol. 3 No. 1 (2025): INDOTECH - April 2025
Publisher : PT. INOVASI TEKNOLOGI KOMPUTER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60076/indotech.v3i1.1212

Abstract

Penelitian ini membahas perancangan sistem yang bertujuan untuk menangani masalah autisme pada anak-anak. Autisme merupakan gangguan yang mempengaruhi kemampuan individu, terutama dalam hal interaksi sosial. Dalam konteks ini, sistem pakar digunakan untuk mentransfer keahlian seorang pakar ke dalam bentuk algoritma yang dapat digunakan untuk diagnosis.Penelitian ini menganalisis dua metode dalam sistem pakar, yaitu Certainty Factor dan Dempster Shafer, yang ditujukan untuk mendiagnosis autisme pada anak. Tujuan utama penelitian ini adalah untuk mengevaluasi dan menentukan metode mana yang paling efektif untuk diimplementasikan dalam aplikasi yang dapat membantu mengklasifikasikan anak-anak dengan autisme.Hasil komparasi menunjukkan bahwanya metode Certainty Factor mencapai tingkat probabilitas di atas 95 %, dibandingkan dengan metode Dempster Shafer dalam komparasi 2 metode yang penulis lakukan. Temuan ini memberikan wawasan yang signifikan mengenai efektivitas kedua metode, serta kontribusi mereka dalam pengembangan sistem pakar untuk diagnosis autisme. Diharapkan penelitian ini dapat menjadi referensi untuk solusi yang lebih baik dalam bidang kesehatan mental anak.
Analysis of normalization technique on multi objective preference analysis method Fristi Riandari; Gabriel Ardi Hutagalung; Ferry Fachrizal
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i4.pp3827-3835

Abstract

Normalization is a critical step in multi-criteria decision analysis (MCDA) because it influences ranking consistency and decision reliability. This study evaluates the effects of four normalization techniques linear max, linear max-min, linear sum, and semi-linear vector, within the multi objective preference analysis (MOPA) framework using a tourism development case involving 18 alternatives and 8 decision criteria with both cost and benefit attributes. The techniques were compared based on ranking behavior, discriminative capability, and robustness using statistical and non-parametric validation. The results show that linear max-min normalization provides the strongest discriminative performance and the most significant statistical results, while semi-linear vector demonstrates high ranking stability and balanced sensitivity. In contrast, linear sum and linear max exhibit lower discriminative capability under the evaluated conditions. Kendall's tau and robustness analyses further confirm that normalization choice significantly affects ranking consistency and decision reliability. These findings provide practical guidance for selecting appropriate normalization techniques and support the development of more reliable MCDA-based decision-making models for complex applications, including sustainable tourism planning.
Transformasi Digital Pengelolaan Data Produksi dan Keuangan Usaha Jamur Tiram Berbasis Dashboard Analitik Virdyra Tasril; Fristi Riandari; Hikmah Adwin Adam; Ramadhanu Ginting; Donny Sanjaya
JURIBMAS : Jurnal Hasil Pengabdian Masyarakat Vol 5 No 2 (2026): Oktober 2026
Publisher : LKP KARYA PRIMA KURSUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62712/juribmas.v5i2.1695

Abstract

Sukamaju Farm is a micro-scale oyster mushroom enterprise in Deli Serdang Regency that manages 9,000 baglogs across nine racks, yet its production and financial records remain manual and unstructured. As a result, per-rack productivity is not monitored, baglog rotation relies on the owner's memory, and net profit cannot be calculated accurately. This community service program aimed to digitally transform the partner's business recording and analysis through mentoring in the use of an analytics dashboard consisting of a per-rack production module, a baglog life-cycle module, and a financial module. A participatory approach was applied in four stages—preparation, socialization, intensive mentoring, and evaluation and sustainability—involving three participants. Achievement was measured through knowledge pre- and post-tests and an independent competency test. The results show that the mean knowledge score increased from 80.0 to 91.1 with an N-gain of 0.56 (moderate category), and all three participants were declared competent in operating the three modules independently. The program equipped the partner to make data-driven business decisions, although the employees' ability to interpret financial reports still needs strengthening.
Perbandingan Metode Teorema Bayes dan Dempster Shafer Menentukan Status Gunung Berapi Ramadhanu Ginting; Fristi Riandari; Afrisawati Afrisawati; indri Sulistianingsih
Jurnal Nasional Teknologi Komputer Vol 5 No 3 (2025): Juli 2025
Publisher : CV. Hawari

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61306/jnastek.v5i3.274

Abstract

Penelitian ini membahas perbandingan dua metode dalam sistem pakar yang digunakan untuk menganalisis status aktivitas gunung berapi. Gunung berapi merupakan struktur geologis yang terbentuk akibat pergerakan magma dari dalam bumi ke permukaan melalui erupsi, yang biasanya disertai pelepasan lava, gas, dan material vulkanik lainnya. Sistem pakar dalam konteks ini bertujuan mentransformasikan pengetahuan seorang ahli ke dalam bentuk algoritma komputasional yang mampu melakukan diagnosis secara otomatis. Dua metode yang dianalisis dalam penelitian ini adalah Teorema Bayes dan Dempster Shafer. Tujuan utama dari studi ini adalah mengevaluasi efektivitas kedua metode dalam menentukan status gunung berapi dan mengidentifikasi metode yang paling akurat untuk diimplementasikan dalam aplikasi sistem pakar. Hasil analisis menunjukkan bahwa metode Dempster Shafer memiliki tingkat akurasi sebesar 98%, lebih tinggi dibandingkan dengan metode Teorema Bayes dalam konteks data gejala yang diuji. Temuan ini menunjukkan bahwa Dempster Shafer lebih unggul dalam klasifikasi status gunung berapi pada kasus ini, serta memberikan kontribusi terhadap pengembangan sistem pakar yang andal dalam mendukung pengambilan keputusan di bidang geologi dan mitigasi bencana alam
Co-Authors Afrisawati Afrisawati Afrisawati Donny Sanjaya Ferry Fachrizal - Gabriel Ardi Hutagalung Hikmah Adwin Adam Indri Sulistianingsih Rahmat Widia Sembiring - Rama Prameswara Ritonga Ramadhanu Ginting Rizky Maulidya Afifa Virdyra Tasril Weno Syechu