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All Journal JIKEM: Jurnal Ilmu Komputer, Ekonomi dan Manajemen Jurnal Penelitian Teknologi Informasi dan Sains Bridge: Jurnal Publikasi Sistem Informasi dan Telekomunikasi Repeater: Publikasi Teknik Informatika dan Jaringan Switch: Jurnal Sains dan Teknologi Informasi Merkurius: Jurnal Riset Sistem Informasi dan Teknik Informatika Neptunus: Jurnal Ilmu Komputer dan Teknologi Informasi Mars: Jurnal Teknik Mesin, Industri, Elektro dan Ilmu Komputer Saturnus: Jurnal Teknologi dan Sistem Informasi
Novriyenni Novriyenni
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Humanities Computer Science & IT Economics, Econometrics & Finance Environmental Science Library & Information Science

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Diagnosa Penyakit pada Jamur Tiram Putih menggunakan Metode Dempster Shafer: ( Studi Kasus : Omah Jamur Tiram Stabat ) Alfina Damayanti; Novriyenni Novriyenni; Rusmin Saragih
Mars : Jurnal Teknik Mesin, Industri, Elektro Dan Ilmu Komputer Vol. 2 No. 5 (2024): Oktober : Mars : Jurnal Teknik Mesin, Industri, Elektro Dan Ilmu Komputer
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/mars.v2i5.358

Abstract

: Oyster mushrooms are one of the popular horticultural products in the community, with a significant increase in demand from year to year. However, farmers often face difficulties in identifying and preventing diseases that attack oyster mushroom plants, which have an impact on production stability. To overcome this problem, this study aims to design an expert system that can diagnose diseases in white oyster mushrooms using the Dempster Shafer Method. This system is designed to provide accurate diagnostic information and solutions to deal with diseases in oyster mushrooms, so as to improve the quality and quantity of production. This study also strengthens previous studies using the Certainty Factor method, with an emphasis on the use of expert knowledge to overcome disease problems in oyster mushroom cultivation. The case study was conducted at Omah Jamur Tiram Stabat, where the results of the implementation are expected to increase the effectiveness and efficiency of oyster mushroom cultivation in the area.
Korelasi Kegiatan MBKM Terhadap Peningkatan Soft Skills Mahasiswa Menggunakan Metode Apriori Dini Anjani; Novriyenni Novriyenni; Zira Fatmaira
Saturnus : Jurnal Teknologi dan Sistem Informasi Vol. 2 No. 4 (2024): Oktober : Saturnus : Jurnal Teknologi dan Sistem Informasi
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/saturnus.v2i4.334

Abstract

Soft skills are non-technical abilities that make a person able to interact and work effectively with others. This study aims to analyze the relationship between student activities of Internships and Certified Independent Study (MSIB) on improving student soft skills using the Apriori method in data mining analysis. this research uses RapidMiner analysis tools to analyze data collected from a total of 539 student data from all over Indonesia, the best association rule has been formed (best rule) which provides information about improving the soft skills of MSIB students. Tests were conducted by determining the minimum support value of 3% (0.03) and the minimum confidence of 30% (0.3). and resulted in 106 association rules. Based on the results of the analysis, it was found that the best rule of 2 itemsets has a support of 39% and a confidence of 67%, the best rule of 3 itemsets has a support of 13% and a confidence of 81%, the best rule of 4 itemsets has a support of 6% and a confidence of 82%, and the best rule of 5 itemsets has a support of 3% and a confidence of 100%. After analyzing data using the Apriori method and RapidMiner application on 539 MSIB student soft skills data, it was found that there was a significant relationship between MBKM activities followed by students and the improvement of their soft skills and these findings also show that the less frequent value is set, the more data can be processed, as well as the minimum support value and confidence value, where the smaller the value determined, the more association results will be issued.
Jaringan Saraf Tiruan dalam Mengidentifikasi Faktor-Faktor Penentu Kesiapan Belajar Anak pada Transisi ke Sekolah Dasar Seri Arihta Br Sitepu; Novriyenni Novriyenni; Ratih Puspadini
Neptunus: Jurnal Ilmu Komputer Dan Teknologi Informasi Vol. 3 No. 3 (2025): Agustus : Neptunus : Jurnal Ilmu Komputer Dan Teknologi Informasi
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/neptunus.v3i3.990

Abstract

The transition of children from early childhood education to elementary school (SD) is a critical phase in their psychological and academic development. During this phase, children face significant challenges, including changes to a more structured learning environment and increasing academic demands. At SDN 055991 in Langkat Regency, this phenomenon is reflected in the difficulties experienced by some students, particularly with basic skills such as reading, writing, and arithmetic, as well as with socializing with peers. These difficulties can impact children's long-term academic and social development. This study aims to identify the key factors influencing children's learning readiness during this transition period, utilizing artificial intelligence (AI) technology. Specifically, this study uses Artificial Neural Networks (ANN) and Decision Trees as tools to analyze the data obtained. The use of this data-driven approach allows for a more in-depth analysis of the complex patterns and relationships between various variables that influence children's learning readiness, such as family factors, social environment, and students' basic skills. This study also references various previous studies demonstrating the effectiveness of backpropagation and Deep Learning algorithms in the context of education and student performance prediction. This approach is expected to provide more precise solutions for understanding children's learning readiness and provide a more accurate picture of the factors contributing to difficulties experienced by students in the transition to elementary school. The results of this study are expected to provide relevant recommendations for parents, educators, and education policymakers to support children's learning readiness and strengthen basic education policies that are adaptive to the needs of students in this digital era.
Diagnosa Penyakit Radang Sendi Menggunakan Metode Dempster Shafer William Jhonatan; Novriyenni Novriyenni; Marto Sihombing
Neptunus: Jurnal Ilmu Komputer Dan Teknologi Informasi Vol. 3 No. 3 (2025): Agustus : Neptunus : Jurnal Ilmu Komputer Dan Teknologi Informasi
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/neptunus.v3i3.1013

Abstract

Rapid technological advancements have brought convenience to various fields, including healthcare. Osteoarthritis (OA) is a chronic degenerative joint disease that often affects the knees and hips, particularly in the elderly, and is a major cause of pain, joint dysfunction, and reduced quality of life. The prevalence of OA increases with age, with risk factors such as obesity, excessive activity, and muscle weakness. Early and accurate diagnosis is essential for appropriate treatment. This study aims to develop a diagnostic system for inflammatory arthritis, specifically osteoarthritis, using the Dempster-Shafer method. This method was chosen because of its ability to combine various evidence and expert beliefs to produce a more accurate diagnosis. By utilizing mathematical proof theory, this system is expected to assist medical personnel in detecting OA symptoms more efficiently. The research findings are expected to contribute to the healthcare sector, particularly in improving the accuracy of osteoarthritis diagnosis, allowing for earlier and more appropriate treatment. This system can also be a supporting tool for doctors and patients in understanding joint health conditions.
Diagnosa Penyakit Preeklamsia Menggunakan Metode Dempster Shafer : Studi Kasus : RSU Bidadari Sabina Eis Zulvahira Nasution; Novriyenni Novriyenni; Hermansyah Sembiring
Bridge : Jurnal Publikasi Sistem Informasi dan Telekomunikasi Vol. 3 No. 3 (2025): Bridge: Jurnal Publikasi Sistem Informasi dan Telekomunikasi
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/bridge.v3i3.610

Abstract

Preeclampsia is one of the most serious complications in pregnancy, characterized by hypertension and proteinuria, and it poses a significant risk of maternal and fetal morbidity and mortality if not detected and managed promptly. Early detection is crucial, yet clinical diagnosis often faces challenges due to the variability of symptoms and uncertainty in medical decision-making. To address this issue, this study aims to develop an expert system for diagnosing preeclampsia by employing the Dempster-Shafer method, which is known for its ability to handle uncertainty and incomplete information in complex domains such as healthcare. A case study was conducted at Bidadari General Hospital, where data on clinical symptoms and patient medical records were collected and analyzed. The development process of the expert system followed systematic stages, including knowledge acquisition from obstetrics specialists, designing the knowledge base, constructing inference rules, and integrating the Dempster-Shafer algorithm for decision support. The system was subsequently tested using real-case scenarios of pregnant women suspected of having preeclampsia. Evaluation results demonstrated that the system achieved an accuracy rate of 92% in differentiating between preeclampsia and eclampsia, based on belief and plausibility measures combined with symptom analysis. These findings indicate that the proposed system can effectively support medical personnel by providing diagnostic recommendations with a high degree of reliability. In addition, the system offers efficiency in the clinical workflow by minimizing diagnostic errors and reducing delays in treatment initiation. Therefore, this expert system has the potential to become a valuable clinical decision support tool for early detection, risk assessment, and management of preeclampsia. Future development may focus on expanding the knowledge base, integrating real-time patient monitoring data, and enhancing usability to ensure broader applicability in diverse healthcare settings.
Korelasi Model Pembelajaran Terhadap Peningkatan Prestasi Belajar Siswa SMA Negeri 1 Kuala Menggunakan Metode Apriori Ame Ananda Br Ginting; Novriyenni Novriyenni; Tio Ria Pasaribu
Repeater : Publikasi Teknik Informatika dan Jaringan Vol. 3 No. 3 (2025): Juli : Repeater : Publikasi Teknik Informatika dan Jaringan
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/repeater.v3i3.616

Abstract

This study aims to analyze the correlation between learning models and student achievement at SMA Negeri 1 Kuala by applying the Apriori algorithm in data mining, using Rapid Miner software as the primary tool for analysis. The research is motivated by the shift in educational approaches from conventional teacher-centered methods toward more innovative strategies such as project-based learning and cooperative learning, which are expected to foster higher levels of student engagement and improve academic outcomes. In many schools, particularly at the secondary level, the choice of learning model, availability of facilities, and attendance rates are crucial factors that shape learning effectiveness and student performance. The data collected in this study include student grades, the types of learning models implemented, school facility conditions, and attendance rates for the 2023/2024 academic year, covering a total of 680 students. The Apriori algorithm was employed to discover hidden patterns and associations among these variables, enabling the identification of relationships between learning factors and academic achievement. By applying Rapid Miner software, the research systematically generated association rules that reflect meaningful correlations in the dataset. The results indicated that the use of the Indonesian language subject in combination with a cooperative learning model, adequate and complete school facilities, and good student attendance was strongly associated with the attainment of an A grade. This finding was supported by a support level of 53.33% and a confidence level of 100%, suggesting a robust and reliable relationship between these factors. The implementation of data mining techniques through Rapid Miner not only allowed for efficient data processing but also provided practical recommendations for educators and school administrators in designing effective instructional strategies.
Penggunaan Metode Rough Set untuk Menentukan Tingkat Kesiapan Siswa dalam Menghadapi ANBK di SMP Negeri 2 Kuala Harninda Br Keliat; Novriyenni Novriyenni; Tio Ria Pasaribu
Repeater : Publikasi Teknik Informatika dan Jaringan Vol. 3 No. 3 (2025): Juli : Repeater : Publikasi Teknik Informatika dan Jaringan
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/repeater.v3i3.619

Abstract

The Computer-Based National Assessment (ANBK) is an essential instrument designed to comprehensively measure student competence, including literacy, numeracy, and character aspects. However, in practice, many students still face various challenges during preparation, such as cognitive limitations, psychological readiness, and technical barriers, which affect their overall readiness to participate in ANBK. This study aims to analyze the readiness level of students at SMP Negeri 2 Kuala by employing the Rough Set method. The variables examined include digital literacy, subject matter understanding, psychological readiness, and school facility support. Data were collected from 250 ninth-grade students through structured questionnaires and subsequently processed using the Rosetta software to perform attribute reduction and generate decision rules. The findings indicate that digital literacy, subject matter understanding, and psychological readiness are the most influential variables in determining student readiness, while facility support serves only as a complementary factor. The extraction process generated seven decision rules with an accuracy level of 100%, which effectively classified students into three readiness categories: highly ready, ready, and less ready. These results confirm that the Rough Set method is highly effective for identifying dominant factors and producing decision rules that can guide schools in developing targeted strategies to enhance student readiness for ANBK.
Implementasi Sistem Pakar Diagnosa Penyakit Tifus dengan Metode Certainty Factor Bintang Wicaksana; Novriyenni Novriyenni; Suci Ramadani
Neptunus: Jurnal Ilmu Komputer Dan Teknologi Informasi Vol. 3 No. 3 (2025): Agustus : Neptunus : Jurnal Ilmu Komputer Dan Teknologi Informasi
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/neptunus.v3i3.1048

Abstract

Typhoid fever is a significant health issue caused by the Salmonella Typhi bacteria, leading to symptoms such as fever, abdominal pain, diarrhea, muscle pain, and serious complications if not treated promptly. A common challenge faced by society is limited access to medical professionals, especially in remote areas, and delays in recognizing symptoms. To address this problem, this study designs and implements a web-based expert system using the Certainty Factor (CF) method, which helps diagnose typhoid fever quickly and accurately. The Certainty Factor method is used to calculate the certainty level of the symptoms experienced by the patient, providing a diagnosis result in the form of early-stage typhoid, mild typhoid, or severe typhoid. The system was developed using PHP programming language and MySQL database, and tested at RSUD Djoelham Binjai City. The research data was obtained from patients at RSUD Djoelham Binjai with a case study on patient number 22. The processing of symptoms through Certainty Factor calculation showed that the patient is most likely to have severe typhoid with a certainty value of 0.9443 or 94.43%. This result proves that the Certainty Factor method can be used to assist in providing an accurate early diagnosis of typhoid fever with a high degree of accuracy.
Diagnosa Penyakit Syndrome pada Anak menggunakan Metode Case Base Reasoning (CBR) Amysa Putri Sitepu; Novriyenni Novriyenni; Muammar Khadapi
Merkurius : Jurnal Riset Sistem Informasi dan Teknik Informatika Vol. 3 No. 6 (2025): November: Merkurius : Jurnal Riset Sistem Informasi dan Teknik Informatika
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/merkurius.v3i6.1139

Abstract

Syndrome is a serious problem in children's health because it has a major impact on growth and development, especially in terms of intelligence and daily activities. Down Syndrome, as one of the most well-known chromosomal disorders, is often the main cause of intellectual developmental disorders, hypotonia, facial dysmorphism, early onset of Alzheimer's disease, and various behavioral disorders. Diagnosing syndrome diseases in children is often difficult due to complex and varied symptoms, requiring lengthy, costly, and time-consuming medical evaluations. This study aims to design a Case-Based Reasoning (CBR)-based expert system for diagnosing syndromes in children, which is expected to help accelerate the disease identification process and provide more effective and efficient solutions. The method used is the development of an expert system with a CBR approach, in which the system performs calculations and matching based on the symptoms selected by the user against the available case base. The results of the study show that from symptom inputs such as wide hands with short fingers, short stature, small head, stunted growth, small lower jaw, abnormal body appearance, and weak joints, the system was able to diagnose Klinefelter syndrome with a percentage of 43.58%. This system can be an alternative for patients or families who have limited time and funds to obtain medical consultations, so that diagnosis and follow-up can be carried out more quickly and efficiently.
Penerapan Algortima K-Means untuk Mengelompokkan Siswa Berdasarkan Tingkat Pemahaman dan Kemandirian Belajar dalam Kurikulum Merdeka Ingke Fuji Utami Br Barus; Novriyenni Novriyenni; Imeldawaty Gultom
Merkurius : Jurnal Riset Sistem Informasi dan Teknik Informatika Vol. 3 No. 6 (2025): November: Merkurius : Jurnal Riset Sistem Informasi dan Teknik Informatika
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/merkurius.v3i6.1141

Abstract

The Merdeka Curriculum implemented in various schools in Indonesia aims to provide flexibility in learning, where students can learn according to their individual needs, interests, and pace. One of the challenges in implementing this curriculum is how to effectively identify classroom activity and student discipline. SD Islamiyah, as a school that implements the Merdeka Curriculum, also faces challenges in understanding variations in student classroom activity and discipline. Some students are able to learn in a disciplined manner with little guidance, while others require more intensive support from teachers. Therefore, a system is needed that can group student data more systematically so that teachers can develop teaching strategies that suit the needs of each group of students. One algorithm that can be used in data grouping is k-means clustering. The K-Means algorithm is a non-hierarchical algorithm derived from the data clustering method. The K-Means algorithm begins with the formation of cluster partitions at the beginning, then iteratively refines these cluster partitions until there are no significant changes in the cluster partitions. The K-Means method partitions data into groups so that data with similar characteristics are placed in the same group and data with different characteristics are grouped into other groups. This method can help group students more accurately based on their Class Activity and Discipline. From the results of the analysis, it was concluded that the student data group with Class Activity was Moderately Active Students, with Discipline being Disciplined, and an average score of 71-80.
Co-Authors Alfina Damayanti Ame Ananda Br Ginting Amysa Putri Sitepu Andini Andini Anjellita Sundari Sumarsono Bintang Wicaksana Dini Anjani Dio Fani Prakasa Eninta Rahayu Barus Fatmaira, Zira Gultom, Imeldawaty Harninda Br Keliat Hermansyah Sembiring Ingke Fuji Utami Br Barus Kadim, Lina Arliana Nur Khadapi, Muammar Khair, Husnul Kiki Adilianti Livia Dirma Pr Marto Sihombing Nurhafieza Nurhafieza Nurul Alya Harahap Putri Mayang Sari Putri Riswana Rahayu Utami Rahmadhani Rahmadhani Ramadani, Suci Ratih Puspadini Rusmin Saragih, Rusmin Sabina Eis Zulvahira Nasution Seri Arihta Br Sitepu Siswan Syahputra Syari, Milli Alfhi Tio Ria Pasaribu William Jhonatan