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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) RABIT: Jurnal Teknologi dan Sistem Informasi Univrab KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer) Systematics Jurnal Teknologi Dan Sistem Informasi Bisnis Jurnal Informasi dan Teknologi Jurnal Informatika Ekonomi Bisnis Community Development Journal: Jurnal Pengabdian Masyarakat JUKI : Jurnal Komputer dan Informatika Journal of Applied Data Sciences Jurnal Computer Science and Information Technology (CoSciTech) Journal of Computer Scine and Information Technology Insearch: Information System Research Journal Jurnal Komtekinfo Jurnal Sistim Informasi dan Teknologi Innovative: Journal Of Social Science Research
Yuhandri Yuhandri
Universitas Putra Indonesia YPTK Padang
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Public Health Social Sciences Other

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Penerapan Metode Simple Additive Weighting dalam Sistem Pendukung Keputusan Pemilihan Jurusan (Studi Kasus di SMK Negeri 1 Kota Solok) Alifia Restu Selvanda; Sumijan Sumijan; Yuhandri Yuhandri
Innovative: Journal Of Social Science Research Vol. 3 No. 4 (2023): Innovative: Journal Of Social Science Research
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/innovative.v3i4.4356

Abstract

Kemajuan teknologi komputer memiliki dampak yang sangat penting dalam kehidupan manusia. Komputer telah menjadi alat yang sangat membantu dalam menjalankan berbagai aspek kehidupan sehari-hari, seperti memudahkan akses informasi melalui internet dan memproses data yang kompleks di lingkungan perkantoran dan pendidikan. Meskipun teknologi komputer telah menjadi bagian tak terpisahkan dari banyak aspek kehidupan, beberapa sektor, termasuk beberapa sekolah, masih belum sepenuhnya memanfaatkan kemajuan teknologi ini, terutama dalam proses pemilihan jurusan untuk siswa baru. Untuk alasan ini, penelitian ini dilakukan dengan tujuan untuk menggali dan menjelaskan proses pemilihan jurusan di SMK Negeri 1 Kota Solok. Dengan pemahaman yang lebih mendalam tentang proses ini, diharapkan penelitian ini dapat memberikan panduan yang lebih baik bagi siswa baru dalam mengambil keputusan yang lebih tepat dalam memilih jurusan. Untuk mencapai tujuan ini, penelitian ini mengadopsi metode Simple Additive Weighting (SAW) dalam proses pemilihan jurusan. Penelitian ini melibatkan 137 siswa dan menggunakan 4 kriteria yang relevan, dengan 6 alternatif jurusan dengan tujuan memberikan rekomendasi yang lebih baik terkait pemilihan jurusan untuk penerimaan siswa baru tahun 2023/2024. Hasil dari penelitian ini tidak hanya berupa rekomendasi jurusan terbaik, tetapi juga menghasilkan sistem pendukung keputusan berbasis web. Sistem ini menerapkan metode Simple Additive Weigthing (SAW) dan dapat membantu siswa dalam mengambil keputusan yang lebih berdasarkan informasi dan efisien terkait pemilihan jurusan mereka. Dengan demikian, diharapkan bahwa penelitian ini akan memberikan manfaat nyata bagi siswa baru dan sekolah itu sendiri dalam meningkatkan kualitas pendidikan serta proses pengambilan keputusan.
Penanganan Celah Keamanan Website dengan Ethical Hacking dan Issaf Menggunakan Acunetix Vulnerability (Studi Kasus di Bkpsdmd Kabupaten Kerinci) Lusi Kestina; Yuhandri Yuhandri; Gunadi Widi Nurcahyo
Innovative: Journal Of Social Science Research Vol. 3 No. 4 (2023): Innovative: Journal Of Social Science Research
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/innovative.v3i4.4357

Abstract

Permasalahan keamanan pada dunia pemerintahan menjadi salah satu sasaran praktik peretas jahat. Kepentingan hacker pada sektor pemerintahan tidak hanya berfokus pada kepentingan ekonomi, tetapi juga kepentingan politik. Salah satu serangan yang sering terjadi adalah web defacement. Badan Kepegawaian dan Pengembangan Sumber Daya Manusia Daerah (BKPSDMD) Kabupaten Kerinci merupakan instansi yang aktif dalam menggunakan teknologi website dalam aktifitasnya. Penelitian ini dilakukan untuk mengukur tingkat keamanan website BKPSDMD dengan menerapkan Ethical Hacking dan ISSAF. Hasil dari penelitian ini akan merekomendasikan dan menerapkan tindakan optimalisasi berdasarkan hasil analisa dari penerapan Ethical Hacking dan ISSAF.
Comparison of Decision Tree and Random Forest Methods in Predicting Oil Palm Productivity After Replanting Sukardi; Yuhandri; Sarjon Defit
Jurnal KomtekInfo Vol. 13 No. 1 (2026): Komtekinfo
Publisher : Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35134/komtekinfo.v13i1.680

Abstract

Oil palm is a strategic commodity in Indonesia that can be affected by various factors such as plant age, soil conditions, rainfall, and maintenance variations between farmers. Over time, oil palm productivity decreases, so it is necessary to predict the productivity of oil palm rejuvenation. Based on this, the purpose of this study is to apply and compare the Decision Tree and Random Forest algorithms to predict the level of oil palm productivity after rejuvenation. The prediction process was carried out at the Koperasi Unit Desa (KUD) Tirta Kencana, Kuantan Singingi Regency. The Decision Tree algorithm is a supervised prediction model, meaning it requires a training dataset whose role replaces past human experience in making decisions. The Random Forest algorithm is also able to present several decision trees used in the prediction process. The dataset in this study amounted to 241 farmer data sourced from the KUD Tirta Kencana in Kuantan Singingi Regency. The comparative results of these two methods show that both the Decision Tree and Random Forest algorithms are capable of predicting precisely and accurately. The comparative results show that the random forest method outperforms the decision tree method with an accuracy of 99%. The contribution of this research provides knowledge with the application of data mining science by comparing the performance of the decision tree and random forest algorithms in the process of plant productivity management at KUD Tirta Kencana. Keywords: Oil Palm Productivity, Data Mining, Decision Tree, Random Forest, Productivity Prediction
Decision Support System for PKH Assistance Recipients Promethee Method Alifcha Ghazian; Yuhandri; Muhammad Ikhlas
Journal of Computer Scine and Information Technology Volume 9 Issue 4 (2023): JCSITech
Publisher : Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35134/jcsitech.v9i4.85

Abstract

The Family Hope Program (PKH) is a program providing conditional social assistance to Beneficiary Families (KPM) who are designated as PKH beneficiary families. With the current development of information technology, decision making can be assisted by using computers equipped with application programs to support the production of the required quality decisions. In selecting families who receive assistance from the Family Hope Program (PKH) using the Decision Support System using the Promethee method. Promethee is a method of determining the order (priority) in multi-criteria analysis, the result of this method is a ranking of alternatives based on the selected criteria. The decision support system application using the Promethee method can determine which disadvantaged families will receive PKH assistance with faster, more accurate and fair calculations and considerations. The results of research using this method using 5 alternatives, there are 3 alternatives with accepted status and 2 alternatives that are rejected. The accepted alternatives were Tiah, Mansyah and Mus Muliadi. Meanwhile, the alternatives that were rejected were Irmansyah and Ainun. By obtaining calculation results from a decision support system using the Promethee method, it can help and make it easier for sub-districts to determine recipients of the Family Hope Program (PKH) for underprivileged families.
Measurement of Health Information Systems Using the McCall Method Dzaki Al Fikri; Yuhandri; Mardison
Journal of Computer Scine and Information Technology Volume 10 Issue 1 (2024): JCSITech
Publisher : Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35134/jcsitech.v10i1.97

Abstract

In an era of technology that continues to develop rapidly, structured and detailed data management is becoming increasingly important. This allows decision makers at the Clinic to easily monitor, evaluate and plan business strategies. The information system measurement application on Klink Mitra Sadona is used to analyze the quality of the electronic registration information service system for patients. This registration system can help patients make it easier to register at the clinic. Based on this, the quality of the health information system will be measured because in this system the level of system quality is not yet known, so as to identify the accuracy, completeness and quality of the software at the clinic. The measurement method in this research uses the McCall Method. The McCall method is a method used to assess the quality of a system. The results of research based on the McCall Method show that the quality of information system measurements is very good with a percentage value of 94%, with the best indicator value, namely efficiency with a result of 72% and the integrity indicator value is the worst indicator with a result of 52%.
Application of the FP-Growth Algorithm in Consumer Purchasing Pattern Analysis Indah Dwi Putri; Yuhandri; Romi Hardianto
Journal of Computer Scine and Information Technology Volume 10 Issue 2 (2024): JCSITech
Publisher : Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35134/jcsitech.v10i2.99

Abstract

Technology is currently used in various ways, one of which is businesses engaged in selling daily products. The right marketing strategy makes knowledge of consumer shopping patterns important to study because consumers are the main actors in carrying out transactions. The more diverse the types of goods sold in a company, the more diverse the resulting consumer spending patterns will be. Data mining is an analysis process that is carried out automatically on complex and large amounts of data to obtain patterns or trends that are generally not realized. The FP-Growth algorithm is an alternative algorithm that can be used to determine the data set that appears most frequently (frequent itemset) in a data set. The method used in this research is the FP-Growth method which is implemented in the PHP programming language and MySQL as the database. Designing a data mining program using the FP-Growth method can analyze and manage consumer purchasing patterns based on goods purchased simultaneously. The data processed in this research is transaction data that has been processed into information so as to gain knowledge in calculating stock of goods sourced from the owner of Toko Asra. From testing this method, results were obtained from the 10 transactions in December 2021, by limiting the minimum support value to 0.2 and minimum confidence to 0.75, 33 patterns of consumer shopping habits were obtained, meaning that 33 products were most frequently purchased by consumers. Designing a data mining program using the FP-Growth method can help analyze consumer purchasing patterns based on items purchased simultaneously. The results of frequent itemset calculations can help find a sequence of combinations that can be used as product recommendations in business decision
Decision Support System for Selecting Casual Daily Workers to Become Permanent Employees Using the Profile Matching Method Eggy Febyanti Edwar; Yuhandri; Syafri Arlis
Journal of Computer Scine and Information Technology Volume 10 Issue 4 (2024): JCSITech
Publisher : Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35134/jcsitech.v10i4.109

Abstract

Information is the result of processing data from one or more sources, which is then processed to provide value, meaning and benefits. In modern times, the use of technology plays a very important role as a means of information and promotion, especially in the field of websites in delivering information. Technological advances in the field of computers are very helpful in the current decision-making process. One method of decision support systems is profile matching. This method is used to determine the assessment in selecting daily employees to become employees. Profile matching is broadly a process of comparing individual competition in job competition so that the difference in competition (also called gap) can be known, the smaller the gap produced, the greater the weight of the value which means that there is a greater chance for employees to occupy the position. After the calculation using the Profile Matching method, the ranking value that meets the requirements is in the alternative with the name of the worker, namely Bakhtiar with a score of 4.535 and is recommended to become a permanent employee. By applying this method, it is very helpful in determining the selection of casual laborers to become permanent employees.
Development of New Identification Formula to Extract Organic Fertilizer Content Based on Organic Fertilizer Image Agung Ramadhanu; Mardison Mardison; Halifia Hendri; Febri Hadi; Larissa Navia Rani; Yuhandri Yuhandri
Journal of Applied Data Sciences Vol 7, No 2: May 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i2.1300

Abstract

Traditional laboratory techniques for examining the nutrient content of organic fertilizers, specifically nitrogen (N), phosphorus (P), and potassium (K), are expensive, time-intensive, and pose environmental hazards. To address these issues, this paper presents a novel, non-destructive, image-based classification algorithm to identify fertilizer nutrient content. The proposed technique integrates color space conversion, unsupervised clustering, texture extraction, and an adapted New Identification Weighting (NIW) method. The NIW is derived from prior probability-based distance measurements and optimized with a balancing weighting factor to improve analytical stability across heterogeneous agricultural images. First, RGB images of fertilizers are converted into the perceptually uniform CIE L*a*b color space, which enhances color distinction under varying lighting conditions. Next, the images are segmented using K-Means clustering, followed by Gray-Level Co-occurrence Matrix (GLCM) extraction to capture textural and structural features. A key innovation of this research is the NIW method, functioning as an adaptive feature prioritization tool that assesses each features contribution to nutrient classification, effectively overcoming the limitations of previous a priori approaches. The system was tested on a dataset of 500 organic fertilizer images, achieving an overall classification accuracy of 97%, demonstrating its effectiveness and robustness. This approach offers a highly accurate and interpretable alternative to conventional chemical testing, making it a feasible, scalable, and affordable field tool for smart farming. By enabling on-site nutrient analysis, it strongly supports sustainable agricultural practices. Future work will focus on enhancing the systems flexibility to varying environmental conditions and integrating this approach into mobile-based diagnostic devices to facilitate real-time decision-making in agriculture.
A Modified Watershed Algorithm for Rice Plant Growth Stage Analysis Teri Ade Putra; Yuhandri Yuhandri; Agung Ramadhanu
Journal of Applied Data Sciences Vol 7, No 2: May 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i2.1117

Abstract

Information technology plays a crucial role in enhancing various sectors, including agriculture. In particular, technological advancements in crop monitoring are essential for sustainable food production, where accurate growth analysis is vital. Image-based approaches have emerged as a promising tool for assessing crop growth, particularly in rice plants. This study aims to enhance rice plant image segmentation using an improved Watershed algorithm, integrating the Laplacian operator and Distance Transform. This study utilizes a Support Vector Machine (SVM) classifier for segmenting and classifying rice plant growth stages, achieving accuracy, precision, recall, and F1-score metrics. The dataset consists of 1080 images of rice plants, with 74 images used for training, 31 for testing, and 975 images for validation. The image processing pipeline involves preprocessing steps such as grayscale conversion, normalization, color segmentation, Otsu thresholding, filtering, and edge detection. Following preprocessing, the Watershed algorithm is applied in two scenarios: the conventional method and the enhanced method with the Laplacian operator and Distance Transform. Performance evaluation is based on accuracy, precision, recall, and F1-score metrics. The results show that the enhanced Watershed algorithm significantly outperforms the conventional method, achieving an accuracy of 99.58%, precision of 80.55%, recall of 79.92%, and an F1-score of 81.50%. While there is a slight imbalance in precision and recall, the model demonstrates reliable performance in identifying rice plant growth. This study confirms that integrating the Laplacian operator and Distance Transform into the Watershed algorithm significantly improves segmentation accuracy, supporting the development of automated monitoring systems in smart farming. Furthermore, this approach opens avenues for application in other crops and diverse environmental conditions.
Improvement of Interpolation Performance with Statistical Method in Total Suspended Solid Identification Hadi Syahputra; Yuhandri Yuhandri; Sumijan sumijan
Journal of Applied Data Sciences Vol 7, No 2: May 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i2.1190

Abstract

Total Suspended Solids (TSS) is one of the key parameters used to determine water quality, which can be observed through the density level of suspended particles. The determination of TSS aims to ensure that river pollution levels can be controlled to maintain good environmental quality. However, the identification of TSS is still performed manually, which requires a relatively long processing time. This condition highlights the need for an effective and efficient identification process. Based on these considerations, this study aims to develop an extraction technique to identify TSS in river water using the Interpolation Mean Square (IMS) algorithm. The development of the extraction technique within the IMS algorithm is crucial for improving the performance of linear interpolation methods. Mean Square is proposed as a parameter in the interpolation process to optimize the extraction algorithm. The segmentation process based on the performance of the IMS algorithm involves exploring and grouping image intensity values. The resulting segmented image clusters are subsequently selected based on the values produced by the Mean Square computation, which are then processed as the final segmentation output. The experimental results show an improvement in the performance evaluation results of the IMS algorithm providing an increase of 7% to 10% over the previous linear interpolation method. The evaluation results produced by the IMS algorithm are 90.19% accuracy, 99.99% sensitivity, and 83.33% specificity. These results indicate that the improved interpolation method presented in the IMS algorithm produces optimal results in determining TSS. Improving the performance of the interpolation method through the development of an IMS-based extraction technique has succeeded in producing optimal identification results. The superiority of the IMS algorithm provides novelty in the development of interpolation techniques for automated segmentation. Furthermore, the findings of this study can effectively support the West Sumatra Environmental Agency in addressing river water pollution issues.
Co-Authors - Hendrick AA Sudharmawan, AA Abdul Azis Said Agung Ramadhanu Akbar Iskandar Alifcha Ghazian Alifia Restu Selvanda Allans Prima Aulia Angga Putra Juledi Arika Juwita Z Ayu Prima Siska Budi Jaya Budi Permana Putra Chairul Imam Darnis, Rahmi Dendi Ferdinal Deno Yulfa Ardian Desi Laidawati Dodi Andre Putra DWI JULISA UTARI Dwika Assrani Dzaki Al Fikri Eggy Febyanti Edwar Eka Naufaldi Novri Eka Sofianti Fahmi Firzada Fajri Ilhami Andrean Febri Hadi Fhajri Arye Gemilang Gunadi Widi Nurcahyo Hadi Syahputra Halifia Hendri Hasanatul Iftitah Hendro Zalmadani Henky Andema Hermanto Heru Rahmat Wibawa Putra Indah Dwi Putri Irvan Okta Mazhona Ismail Virgo Jefdy Kurniawan Johan Danu Wijaya Jufriadif Na`am, Jufriadif Julius Santony Julius Santony K Kadrahman Larissa Navia Rani Lc Granadi Suhaidir Lidia K Simanjuntak Lova Endriani Zen Lusi Kestina M Ilham Aldyno M Mutia Mardison Mardison Mardison Mesran, Mesran Mohammad Guntur Montesna Muhammad Arif Zikir Risky Muhammad Ihksan Muhammad Ikhlas Musli Yanto Nandra Sunaryo Nasma Yeni Nasution, Annio Indah Lestari Nuning Kurniasih R Rahmiyanti Rafi Septiawan Putra Ragil Ardiansyah Rahmad Dian Rio Andika Malik Riski Randa Hidayatullah Rivo Stephano Roby Nurbahri Romi Hardianto Ronda Deli Sianturi Rovidatul S Salmiati S Sumijan Sahat Sonang Sitanggang Salman Alfarisi Salimu Sarjo Defit Sarjon Defit Sarjon Defit Septiana Vratiwi Setiawan, Adil Silfia Andini Sri Amalia Harahap Sri Dewi Stefani Hardiyanti Putri Subrianto Chandra Sukardi Sumijan Sumijan Sumijan Sumijan Sumijan Sumijan Sumijan Syafri Arlis Syahid Hakam Abdul Halim Syaljumairi, Raemon Teddy Winanda Teguh Junaidi Teri Ade Putra Tessa Y M Sihite Wenni Afrodita Willy Eka Septian yanto, heri Yendi Putra Yosua Ade Pohan Yundari, Yundari Yuniko Fauzan Yusma Elda Zupri Henra Hartomi