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All Journal Jurnal Minfo Polgan (JMP) Journal of Social Research STORAGE: Jurnal Ilmiah Teknik dan Ilmu Komputer Cerdika: Jurnal Ilmiah Indonesia
Franky Gerald Cliford Manoppo
Politeknik Negeri Manado
Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Industrial & Manufacturing Engineering Library & Information Science Mathematics Public Health Social Sciences

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Sistem Pakar untuk Identifikasi Risiko Proyek Teknologi Informasi Berbasis Metode Fuzzy Logic Era Sari Munthe; Anwar T. Sitorus; Franky Gerald Cliford Manoppo; Devi Puspita Sari; Filda Angellia
Jurnal Minfo Polgan Vol. 13 No. 2 (2024): Artikel Penelitian
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/jmp.v13i2.14217

Abstract

Dalam pengembangan proyek teknologi informasi, identifikasi risiko menjadi tahap penting yang dapat memengaruhi keberhasilan proyek. Risiko yang tidak diidentifikasi sejak awal dapat menyebabkan peningkatan biaya, keterlambatan waktu, atau bahkan kegagalan total proyek. Penelitian ini bertujuan untuk mengembangkan sistem pakar berbasis metode Fuzzy Logic untuk identifikasi risiko proyek teknologi informasi secara akurat dan dinamis. Sistem ini dirancang untuk membantu manajer proyek dan tim dalam mengantisipasi serta mengelola potensi risiko dengan lebih efektif. Metode Fuzzy Logic dipilih karena kemampuannya dalam mengolah data yang bersifat tidak pasti dan ambigu, yang sering kali muncul dalam penilaian risiko. Penggunaan Fuzzy Logic memungkinkan penilaian risiko yang lebih fleksibel, dengan mempertimbangkan berbagai faktor risiko proyek seperti kompleksitas teknis, ketidakpastian anggaran, dan jadwal proyek. Sistem pakar ini terdiri dari beberapa tahapan utama: pengumpulan data risiko, penyusunan aturan fuzzy, dan penerapan metode inferensi fuzzy untuk menentukan tingkat risiko. Data risiko diperoleh melalui wawancara dengan pakar proyek teknologi informasi serta tinjauan literatur terkait. Hasil uji coba menunjukkan bahwa sistem pakar berbasis Fuzzy Logic ini mampu mengidentifikasi tingkat risiko proyek dengan akurasi tinggi dan menyediakan informasi yang bermanfaat untuk pengambilan keputusan. Diharapkan bahwa implementasi sistem ini dapat meminimalkan risiko yang tidak diantisipasi dan mendukung keberhasilan proyek teknologi informasi di berbagai sektor. Dengan demikian, sistem pakar ini berpotensi menjadi alat bantu yang efektif bagi manajer proyek dalam mengelola risiko proyek secara proaktif.
Implementation of Data Logging and Historical Graphs of Furnace Temperature at the Electrical Engineering Department Laboratory, Manado State Polytechnic Mohamad Fathan Masloman; Gilang Ramadhan S. Luawo; Jim Mardin Wanimbo; Sintya Paula Junaedy; Franky Gerald Cliford Manoppo
Journal of Social Research Vol. 5 No. 7 (2026): Journal of Social Research
Publisher : International Journal Labs (AHU-0028405-AH.01.14 Tahun 2022)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55324/josr.v5i7.3244

Abstract

Accurate temperature monitoring is essential in laboratory and industrial operations involving furnaces to ensure process consistency, prevent overheating, and support safety compliance. Traditional monitoring methods, such as analog gauges or standalone digital thermometers, provide only instantaneous readings and fail to store historical data or visualize trends, limiting experimental reproducibility and quality control. This study aims to design, implement, and evaluate a low-cost, real-time data logging and historical graphing system for furnace temperature monitoring at the Electrical Engineering Laboratory of Politeknik Negeri Manado. The system integrates Type K thermocouples, MAX6675 thermocouple-to-digital modules, Arduino Uno microcontrollers, and a Python-based visualization application. The research employed an applied R&D approach, including stages of literature review, requirements analysis, system design, implementation, testing, and evaluation. Controlled experiments were conducted at five temperature set points, and a four-hour continuous logging test was performed to assess accuracy, logging continuity, and graphical performance. Results indicate that the system achieved a mean absolute error of 1.1°C, maintained uninterrupted data logging over 14,400 samples, and rendered real-time graphs with stable CPU usage. The findings confirm that the integrated system meets design specifications, providing a reliable and educationally valuable tool for laboratory use. This study concludes that low-cost, modular data logging solutions can effectively enhance instrumentation education and support precise thermal monitoring, with potential for further improvements in multi-channel and cloud-based monitoring systems.
Literature Study on The Development of Artificial Intelligence in Education Franky Gerald Cliford Manoppo; Shallom Aurelly Warouw; Claudio Reinaldo Woran; Jovan Putra Sihombing; Julianus Melale
Cerdika: Jurnal Ilmiah Indonesia Vol. 6 No. 3 (2026): Cerdika: Jurnal Ilmiah Indonesia
Publisher : Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59141/cerdika.v6i3.3389

Abstract

Background: This literature review aims to provide a comprehensive overview of the direction of AI development in education and offer strategic recommendations for sustainable, ethical, and responsible implementation. Objective: This study is a literature review aimed at examining and analyzing the development and application of artificial intelligence (AI) in the field of education. Methods: A quantitative approach was employed through a survey involving 450 respondents representing students, lecturers, and administrative staff at University X. The survey was complemented by a six-month content analysis of the institution’s official social media accounts (January–June 2024). Data were analyzed using descriptive statistics and simple regression. Results: However, despite the substantial opportunities offered, the implementation of AI also faces several challenges, including infrastructure limitations, ethical issues related to personal data usage, and the readiness of human resources within educational environments. Conclusion: Based on an extensive review of various scientific sources, the study finds that AI technology has been widely adopted across multiple educational aspects, including adaptive learning systems, intelligent tutoring systems, and learning analytics. AI has demonstrated a significant contribution to enhancing the effectiveness and efficiency of learning processes, strengthening personalized learning, and simplifying academic administrative management.
IMPLEMENTASI ALGORITMA NAÏVE BAYES UNTUK PREDIKSI RISIKO RELAPS PADA SISTEM MONITORING PASIEN REHABILITASI NARKOBA Steve Jeremy Rarumangkay; Yonathan Kazu Datumbanua; Levi Elia Pitoy; Marike Amelda Silvia Kondoj; Franky Gerald Cliford Manoppo
STORAGE: Jurnal Ilmiah Teknik dan Ilmu Komputer Vol. 5 No. 3 (2026): Agustus
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/storage.v5i3.8209

Abstract

Penelitian ini bertujuan mengimplementasikan algoritma Gaussian Naïve Bayes pada sistem Analisis Risiko Relaps untuk memprediksi risiko relaps pasien rehabilitasi narkoba secara cepat, terstruktur, dan berbasis data. Sistem dikembangkan menggunakan Python, Streamlit, dan MySQL dengan pendekatan Research and Development. Tahapan penelitian meliputi pengumpulan data, prapemrosesan, encoding, standardisasi, SMOTE, pembagian data 80:20, pelatihan model, evaluasi, dan implementasi sistem. Variabel prediktor mencakup usia, riwayat pakai, hasil tes urine, lama pakai, status kerja, dan dukungan keluarga. Dari 87 data sampel pemeriksaan dengan 76 pasien unik, sistem mengklasifikasikan 44 pasien berisiko tinggi dan 32 pasien berisiko rendah. Evaluasi awal pada 18 data uji menghasilkan accuracy, precision, recall, dan F1-score sebesar 1,00. Meskipun hasil ini menunjukkan performa model yang sangat baik pada data uji, ukuran sampel pengujian yang masih terbatas menyebabkan hasil tersebut belum dapat digeneralisasi secara luas. Oleh karena itu, sistem masih memerlukan pengujian lebih lanjut menggunakan dataset yang lebih besar, lebih beragam, serta validasi silang untuk memastikan stabilitas dan generalisasi model sebelum diterapkan secara operasional dalam skala luas.
Co-Authors Anwar T. Sitorus Claudio Reinaldo Woran Devi Puspita Sari Era Sari Munthe Filda Angellia Gilang Ramadhan S. Luawo Jim Mardin Wanimbo Jovan Putra Sihombing Julianus Melale Levi Elia Pitoy Marike Amelda Silvia Kondoj Mohamad Fathan Masloman Shallom Aurelly Warouw Sintya Paula Junaedy Steve Jeremy Rarumangkay Yonathan Kazu Datumbanua