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All Journal International Journal of Electrical and Computer Engineering Elektron Jurnal Ilmiah TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Explore: Jurnal Sistem Informasi dan Telematika (Telekomunikasi, Multimedia dan Informatika) SITEKIN: Jurnal Sains, Teknologi dan Industri Riau Journal of Computer Science JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Komputer Terapan Jurnal Mantik Penusa Rang Teknik Journal Digital Zone: Jurnal Teknologi Informasi dan Komunikasi JURTEKSI Jurnal Teknologi Informasi dan Pendidikan Systematics Jurnal Teknologi Dan Sistem Informasi Bisnis Jurnal Sistem informasi dan informatika (SIMIKA) Jurnal Sistim Informasi dan Teknologi Jurnal Informasi dan Teknologi Jurnal Informatika Ekonomi Bisnis Journal of Applied Engineering and Technological Science (JAETS) JSR : Jaringan Sistem Informasi Robotik Jurnal Teknik Informatika C.I.T. Medicom Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Journal of Applied Data Sciences Jurnal Computer Science and Information Technology (CoSciTech) Journal of Computer Scine and Information Technology Bulletin of Computer Science Research Jurnal Teknoif Teknik Informatika Institut Teknologi Padang Jurnal Komtekinfo Jurnal Sistim Informasi dan Teknologi Innovative: Journal Of Social Science Research Jurnal Informatika Ekonomi Bisnis RJOCS (Riau Journal of Computer Science) Jurnal Elektronika dan Teknik Informatika Terapan Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen)
Gunadi Widi Nurcahyo
Universitas Putra Indonesia YPTK Padang
Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Public Health Other

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Analisis Metode Forward Chaining dan Certainty Factor untuk Diagnosa Penyakit pada Ibu Hamil Yasmin, Nabilla; Yuhandri, Yuhandri; Nurcahyo, Gunadi Widi
Bulletin of Computer Science Research Vol. 5 No. 5 (2025): August 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i5.756

Abstract

The high number of complications that occur during pregnancy and childbirth has the potential to significantly increase the risk of morbidity and mortality in pregnant women. The Maternal Mortality Rate (MMR) reflects the condition of pregnant, delivering, and postpartum mothers, which remains relatively high and is a major concern in the health sector. Based on this, this study aims to develop and evaluate an Expert System based on the Forward Chaining and Certainty Factor methods to diagnose diseases in pregnant women at an early stage, thereby providing fast and accurate medical decision support and minimizing the risk of complications during pregnancy. The Forward Chaining and Certainty Factor methods were chosen for their ability to handle rule-based inference processes and provide certainty level calculations in the diagnosis results. Forward Chaining is used to find solutions based on the symptoms entered by users, while the Certainty Factor helps assign confidence weights to the generated diagnosis. The dataset in this study consists of 30 data samples with 30 types of symptoms experienced by patients as variables. The results show that the Forward Chaining and Certainty Factor methods are capable of producing disease diagnoses in pregnant women with an accuracy rate of 95%. The contribution of this research is to improve the quality of maternal health services through fast and accurate diagnoses by medical personnel and to assist pregnant women in obtaining an initial diagnosis of common diseases during pregnancy.
APPLICATION OF THE PROFILE MATCHING METHOD IN RECOMMENDING DOCTORAL CANDIDATES FOR LECTURER (CASE STUDY AT STMIK ROYAL) Amin, Muhammad; Nurcahyo, Gunadi Widi; Yunus, Yuhandri
JURTEKSI (jurnal Teknologi dan Sistem Informasi) Vol. 10 No. 3 (2024): Juni 2024
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Royal Kisaran

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33330/jurteksi.v10i3.3055

Abstract

Abstract: The advancement of information technology and knowledge has facilitated the production of quality information. The use of information technology has penetrated all fields, especially in the teaching domain at higher education institutions, aiding in valuable decision-making processes. This research focuses on STMIK Royal Kisaran, which faces challenges in increasing the number of doctoral-educated lecturers. To address this limitation, the study explores the implementation of a Decision Support System (DSS) using the Profile Matching method. Lecturers in higher education play a crucial role in providing education, conducting research, and contributing to society. In an effort to enhance the qualifications of lecturers, this research designs a Decision Support System using the Profile Matching method. The aim of this research is to provide recommendations for prospective lecturer candidates to pursue a Doctoral degree based on criteria factors such as length of service, functional position, research score, dedication score, age, and recognition score. Data from 46 lecturers at STMIK Royal Kisaran who meet the criteria are used to test the validity and effectiveness of the Decision Support System (DSS). Through structured analysis, it is demonstrated that the Decision Support System using the Profile Matching method successfully provides recommendations for suitable lecturer candidates to pursue doctoral studies.Keywords : decision support systems; higher education; information Technology; lecturer qualifications;  profile matching.  Abstrak: Kemajuan teknologi informasi dan ilmu pengetahuan telah menghadirkan kemudahan dalam menghasilkan informasi yang berkualitas, penggunaan teknologi informasi sudah memasuki segala bidang terutama bidang pengajaran pada perguruan tinggi dan membantu pengambilan keputusan yang bernilai. Penelitian ini berfokus pada STMIK Royal Kisaran yang mengalami kendala dalam meningkatkan jumlah dosen berpendidikan Doktor. Untuk mengatasi keterbatasan tersebut, penelitian ini mengeksplorasi penerapan Sistem Pendukung Keputusan (DSS) dengan menggunakan metode Profile Matching. Dosen pada pendidikan tinggi mempunyai peran penting dalam memberikan pendidikan, melakukan penelitian, dan memberikan kontribusi kepada masyarakat. Dalam upaya meningkatkan kualifikasi dosen, penelitian ini merancang Sistem Pendukung Keputusan dengan menggunakan metode Profile Matching. Penelitian ini bertujuan untuk memberikan rekomendasi kandidat calon dosen untuk mengejar gelar Doktor dengan berlandaskan faktor kriteria seperti lama kerja, jabatan fungsional, nilai penelitian, nilai pengabdian, umur, dan nilai rekognisi. Data dari 46 dosen STMIK Royal Kisaran yang memenuhi kriteria digunakan untuk menguji validitas dan efektivitas Sistem Pendukung Keputusan (SPK). Melalui analisis terstruktur, menunjukkan bahwa Sistem Pendukung Keputusan menggunakan metode Profile Matching berhasil memberikan rekomendasi calon dosen yang layak direkomendasikan untuk melanjutkan studi ke jenjang Doktor.Kata Kunci : kualifikasi dosen; pencocokan profil; pendidikan yang lebih tinggi; sistem pendukung keputusan; teknologi Informasi.
Development of extraction features for Detecting Adolescent Personality with Machine Learning Algorithms Wisky, Irzal Arief; Defit, Sarjon; Nurcahyo, Gunadi Widi
JOIV : International Journal on Informatics Visualization Vol 8, No 3-2 (2024): IT for Global Goals: Building a Sustainable Tomorrow
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.3-2.3091

Abstract

This study aims to develop a Natural Language Processing (NLP)-based feature extraction algorithm optimized for personality type classification in adolescents. The algorithm used is TF-IDF + N-Gram Z, which combines Term Frequency-Inverse Document Frequency (TF-IDF) with the N-Gram Z technique to improve the feature representation of the analyzed text. TF-IDF functions to measure the importance of words in a document, while N-Gram Z enriches the context by considering the order of words that appear sequentially. The dataset in this study consists of 3,200 sentences generated by adolescent respondents through a survey designed to explore aspects of their personality. After the feature extraction process is complete, three variants of the Naïve Bayes method are applied for classification, namely Multinomial Naïve Bayes, Bernoulli Naïve Bayes, and Complement Naïve Bayes. Each variant has distinctive characteristics in handling certain data types, such as binomial and multinomial data. The results of the study show that the combined TF-IDF + N-Gram Z algorithm can produce highly representative features, as evidenced by high classification performance. The Multinomial Naïve Bayes and Complement Naïve Bayes variants each achieved 98% accuracy. These findings provide significant contributions to the development of NLP-based personality classification methods for Detecting Adolescent Personality. The combination of the TF-IDF + N-Gram Z algorithm with various Naïve Bayes variants produces an exceedingly high level of accuracy and can be applied in practice in the fields of psychology and adolescent education.
Implementasi Algoritma Apriori dalam Data Mining untuk Optimalisasi Stok Obat di Apotik Parinduri, Rezti Deawinda; Defit, Sarjon; Nurcahyo, Gunadi Widi
Jurnal KomtekInfo Vol. 11 No. 3 (2024): Komtekinfo
Publisher : Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35134/komtekinfo.v11i3.544

Abstract

Data Mining memainkan peran penting dalam mengelola dan menganalisis data besar untuk menemukan pola tersembunyi yang mendukung pengambilan keputusan strategis. Algoritma Apriori, yang dikenal untuk menemukan aturan asosiasi dalam data, menjadi alat yang sangat penting di berbagai sektor, termasuk sektor kesehatan. Dalam pengelolaan stok obat di apotek, terdapat tantangan signifikan seperti kelebihan stok, kekurangan stok, dan risiko kedaluwarsa obat, yang semuanya memerlukan solusi yang tepat dan canggih. Penelitian ini bertujuan untuk menerapkan Algoritma Apriori dalam Data Mining guna meningkatkan efektivitas pengelolaan stok obat, dengan fokus pada beberapa aspek kunci: pertama, memantau dan menganalisis pola pembelian obat secara mendalam; kedua, meningkatkan tata kelola stok melalui penerapan sistem monitoring otomatis yang terintegrasi dengan algoritma tersebut; dan ketiga, mengurangi tingkat kedaluwarsa obat melalui analisis data transaksi yang lebih komprehensif. Data transaksi yang digunakan dalam penelitian ini berasal dari PT Enseval Putera Megatrading Tbk. Cabang Padang, yang meliputi periode 3-7 Juni 2024. Data ini dianalisis menggunakan Microsoft Excel 2010 untuk pengolahan awal dan disimulasikan lebih lanjut dengan RapidMiner untuk memvalidasi hasil. Algoritma Apriori diterapkan untuk menentukan stok obat yang optimal melalui proses yang mencakup penentuan minimum support sebesar 3% dan confidence sebesar 40%, serta eliminasi itemset yang tidak relevan atau yang tidak memenuhi kriteria. Hasil dari analisis ini berhasil menemukan enam aturan asosiasi yang dapat digunakan untuk meramalkan stok obat secara lebih efektif dan efisien. Implementasi Algoritma Apriori diharapkan dapat secara signifikan meningkatkan efisiensi dalam manajemen stok obat, mengurangi risiko kelebihan atau kekurangan stok, serta meminimalkan masalah kedaluwarsa obat. Lebih dari itu, penelitian ini juga berkontribusi pada pengembangan pengetahuan ilmiah dalam bidang Data Mining dan manajemen stok obat, serta memberikan landasan yang kuat bagi penelitian lanjutan dan aplikasi praktis dalam konteks yang serupa. Dengan demikian, hasil penelitian ini tidak hanya memberikan solusi praktis untuk masalah pengelolaan stok obat, tetapi juga memperluas cakrawala pengetahuan dalam penggunaan teknik Data Mining untuk tujuan manajerial di bidang kesehatan.
Co-Authors A Alfarisdon AA Sudharmawan, AA Abdi Rahim Damanik Afifah Cahayani Adha Afriosa Syawitri Agung Ramadhanu Ahmad Zamsuri, Ahmad Alexyusandria alexyusandria Alfarisdon, A Ali Djamhuri Andi, Muhammad Yusril Haffandi Anggraini, Siska Dwi Anita Sindar Apriade Voutama Ardia Ovidius ardialis Ardiani, Novia Sutra Asyhari, Ahmad Aulia Mardhatilla Ayudia, Dina Ayunda, Afifah Trista Bayu Rianto Billy Hendrik Boy Sandy Dwi Nugraha.H Breinda, Engla Budayawan, Khairi Budiarti, Lela Bufra, Fanny Septiani Candra Putra Cyntia Lasmi Andesti Cyntia Trimulia Damanik, Abdi Rahim Daniel Theodorus Darma Yunita Darmawi Darnis, Rahmi Dedi Irawan Deri Marse Putra Dina Ayudia Dinda Permata Sukma DWI JULISA UTARI Dwi Utari Iswavigra Dyan Mardinata Putra Eka Putra, Dian Elfina Novalia Erizke Aulya Pasel Faisal Roza Fajri Karim Fanny Septiani Bufra Fauzan Azim Fauzi Erwis Febriani, Widya Febrina, Yerri Kurnia Fernando Ramadhan Fitriani, Yetti Fortia Magfira Gaja, Rizqi Nusabbih Hidayatullah Hafid Dwi Adha Handika, Yola Tri Hartati, Yuli Hasni, Salmi Hazlita, H Hendrik, Billy Honestya, Gabriela Humairoh, Putri Idir Fitriyanto Idir Ilham Effendi Indah Savitri Hidayat INTAN NUR FITRIYANI Ipri Adi Ira Nia Sanita Irzal Arief Wisky Jefri Rahmad Mulia Johan Harlan Jufri, Fikri Ramadhan Jufriadif Na`am, Jufriadif Jufriadif Na’am Juliantho, Dwana Abdi Julius Santoni Julius Santony Julius Santony Julius Santony Julius Santony Julius Santony Karim, Fajri Khelvin Ovela Putra Kholil, Muhammad Irvan Larissa Navia Rani Leony Lidya Lidia Sutra Lova Endriani Zen Lubis, Fitri Amelia Sari Lusi Kestina Luth Fimawahib M Mutia M, Mutia M. Almepal Wanda M. Ibnu Pati Mardayatmi, Suci Mardison Mardison Marfalino, Hari Meilinda Sari Meilinda Sari Melissa Triandini Miftahul Hasanah Miftahul Hasanah, Miftahul Miftahul Mardiyah Mike Zaimy Muhammad Amin Muhammad Irvan Kholil Nabila, Tuti Nadia, Nadia Aini Hafizhah Nadya Alinda Rahmi Nasution, Amir Salim Khairul Rijal Nia Nofia Mitra Nissa, Ika Ima Nst, Ely Nurhalizah Nur Azizah Nur, Rofil M Nurdini, Siti Nurhadi Parinduri, Rezti Deawinda Pati, Muhammad Ibnu Pebriyanti, Defi Petti Indrayati Sijabat Puji Chairu Sabila Putra, Akmal Darman Putra, Deri Marse Putra, Dyan Mardinata Putri Humairoh Putri, Stefani Putri, Yozi Aulia Putut Wicaksono, Putut Radillah, Teuku Rafiska, Rian Rahmad Supriadi Rahman, Zumardi Ramadhanu, Agung Riati, Itin Rika Apriani Rika Apriani, Rika Ririn Violina Ritna Wahyuni Rizka Hafsari Rizki Mubarak Roby Nurbahri Roni Salambue Rovidatul Rozakh, Muhammad Rusnedy, Hidayati Rustam, Camila S Sumijan Sabil, Muhammad Sahari Sahari Sahri, Alfi Sajida, Mayang Sandi Alam Sandrawira Anggraini Sani, Rafikasani Santriawan, Aji Sari, Fitri P. Sarjon Defit Sarjon Defit Sarjon Defit Septiana Vratiwi Sharon Sintia Sintia Siregar, Diffri Siregar, Fajri Marindra Sisi Hendriani Siska Dwi Anggraini Siti Nurdini Sovia, Rini Sri Handayani Sri Layli Fajri Stefani Hardiyanti Putri Suci Mardayatmi Sumijan Sumijan Sumijan Sumijan Sumijan Sumijan Sumijan Sumijan Sumijan, S Suri, Melati Rahma Sutra, Lidia Syafri Arlis Tesa Vausia Sandiva Ulfa, Ulia Ulfatun Hasanah Ulia Ulfa Verdian, Ihsan Vratiwi, Septiana W Wahyudi Wahyu, Fungki Wahyudi Wahid Wahyudi Wahyudi Wendi Robiansyah Weri Sirait Widya Febriani Yasmin, Nabilla Yeng Primawati Yerri Kurnia Febrina Yetti Fitriani Yolla Rahmadi Helmi Yoni Aswan Yuhandri Yuhandri Yuhandri Yuhandri Yuhandri Yuhandri Yuhandri, Yuhandri Yuhandri Yunus Yuhandri, Y Yuli Hartati Yulihartati, Sandra Yunita Cahaya Khairani Yunus, Yuhandri Yuyu, Yuhandri