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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) JURNAL SISTEM INFORMASI BISNIS TEKMAPRO Journal of Industrial Engineering and Management Infotech Journal UNEJ e-Proceeding NUMERICAL (Jurnal Matematika dan Pendidikan Matematika) Jurnal Inotera INTECOMS: Journal of Information Technology and Computer Science Jurnal Teknologi Informasi dan Multimedia International Journal of Economics, Business and Accounting Research (IJEBAR) JATI (Jurnal Mahasiswa Teknik Informatika) Journal Cerita: Creative Education of Research in Information Technology and Artificial Informatics Journal of Intelligent Decision Support System (IDSS) Jurnal Informa: Jurnal Penelitian dan Pengabdian Masyarakat Tematik : Jurnal Teknologi Informasi Komunikasi Infotech: Journal of Technology Information Infokes : Jurnal Ilmiah Rekam Medis dan Informasi Kesehatan Idealis : Indonesia Journal Information System Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Multicience INTELEKTIVA TIERS Information Technology Journal Journal of Dinda : Data Science, Information Technology, and Data Analytics Journal Computer Science and Informatic Systems : J-Cosys International Journal Software Engineering and Computer Science (IJSECS) Jurnal Teknik Informatika Duta.com : Jurnal Ilmiah Teknologi Informasi dan Komunikasi Duta Abdimas: Jurnal Pengabdian Masyarakat Proceeding of International Conference Health, Science And Technology (ICOHETECH) Prosiding Seminar Nasional Teknologi Informasi dan Bisnis SATIN - Sains dan Teknologi Informasi SmartComp Jurnal Informatika Polinema (JIP) Teknologi : Jurnal Ilmiah Sistem Informasi Jurnal Informatika Dan Tekonologi Komputer

SRI SUMARLINDA

Universitas Duta Bangsa Surakarta

Author-ID : 5295566

Arts Humanities Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Physics Public Health Social Sciences Veterinary Other

Published : 71 Documents Claim Missing Document

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Sistem Informasi Monitoring Perawatan Kendaraan Operasional Berbasis Web di PT. Batik Semar Surakarta Dewangga Ranggi Wiku; Sri Sumarlinda; Hanifah Permatasari
Jurnal DutaCom Vol 14 No 2
Publisher : LPPM Universitas Duta Bangsa Surakarta

Dalam sebuah perusahan untuk menunjang salah satu aktifitas maka di perlukan kendaraan operasional, begitu juga di PT. Batik Semar Surakarta yang menyediakan beberapa kendaraan operasional yang berupa mobil niaga atau mobil box dan mobil konvensional atau mobil penumpang. Dengan adanya kendaraan operasional maka harus ada sebuah perawatan agar layak dan aman saat dipakai dan tidak terjadi hal hal yang diingikan saat di pakai, maka diperlukan sebuah sistem informasi berbasis web untuk memonitoring semua kendaraan yang bisa melihat semua history dari kendaraan tersebut , mulai dari pengecekan kendaraan, pemakaian kendaraan, serta perbaikan kendaraan. Untuk memperoleh data-data yang diperlukan selama penelitian, penulis menggunakan beberapa metode, antara lain: wawancara, observasi dan studi pustaka. Data yang diperoleh kemudian dianalisis dan digambarkan dengan menggunakan metode DFD(Data flow Diagram). Hasil akhir dari penelitian ini adalah berupa monitoring kendaraan operasional yang dapat membantu bagian umum untuk mempermudah dalam monitoring perawatan kendaraan operasional di PT. Batik Semar Surakarta.

IMPLEMENTATION FUZZY INFERENCE SYSTEM (FIS) FOR IDENTIFICATION IT ENTERPRENEURSHIPS BASED ON STUDENTS POTENTIAL WIJI LESTARI; INDRA HASTUTI; SRI SUMARLINDA
JURNAL EKONOMI, SOSIAL & HUMANIORA Vol 3 No 11 (2022): INTELEKTIVA : JURNAL EKONOMI, SOSIAL DAN HUMANIORA - EDISI JULI 2022
Publisher : KULTURA DIGITAL MEDIA ( Research and Academic Publication Consulting )

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This study aims to produce Fuzzy Inference System for mapping information technology (IT) entrepreneurship of students potential. This system application will acquire competence and knowledge of the IT entrepreneurs. This study will provide benefits to further increase the interest and motivation of students to become IT entrepreneurs. Results in the first phase is the mapping Entrepreneurships of information technology into the 5 types technopreneurships namely Software Application Developer, Data Analyst, Computer System & Network Engineer, Graphics Designer & Animator and Multimedia System Developer. Mapping based Academic Potential Test and Personal Characteristics (Entrepreneurships values). The results of the mapping used as domain expertise to develope systems based on Fuzzy Inference System. Result in second phase is the Mamdani Fuzzy Inference System with knowledge base, rule of system and architecture of systemfor identification IT Entrepreneurships potential of students based on Academic Potential Test and characteristics personal (entrepreneurships values).

Decision support system for lecturer publication mapping using k-means clustering method Sri Sumarlinda; Wijiyanto Wijiyanto; Wiji Lestari
Journal of Intelligent Decision Support System (IDSS) Vol 5 No 4 (2022): Desember: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

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Publication is an important tridharma activity for lecturers. This study aims to produce a clustering model using the K-Means algorithm which was built for ease of operation of publications. The method used is research and development which includes the stages of data collection, data preprocessing, clustering process and cluster analysis. The input data consists of 87 with 8 attributes, namely the number of journal articles indexed by Sinta, the number of journal articles indexed by Scopus, the number of citations in Scopus, the H-index in Scopus, the number of articles in indexed journals in Google Scholar, the number of citations in Google Scholar, the H-index in Google Scholar and H-index10 in Google Scholar. The K-Means algorithm is used with 3 clusters and 100 epochs. The clustering results are divided into 3 clusters, namely cluster 1 with 17 members, cluster 2 with 32 members and cluster 3 with 38 members. Clustering with 5 clusters produces cluster 1 with 5 members, cluster 2 with 12 members, cluster 3 with 20 members, cluster 4 with 18 and cluster 5 with 32 members. The results of the cluster analysis show that the clustering process with 3 clusters is improved and the academic application is better than clustering with 5 clusters.

Application Of Mathematical Morphology Algorithm For Image Enhancement Of Breast Cancer Detection Wiji Lestari; Sri Sumarlinda
Proceeding of International Conference on Science, Health, And Technology Proceeding of the 1st International Conference Health, Science And Technology (ICOHETECH)
Publisher : LPPM Universitas Duta Bangsa Surakarta

This study aims to produce an image processing application using Mathematical Morphology to improve the quality of the digital image for breast cancer detection. Medical image is an image produced or used in the medical field. Improving medical image quality is very useful for diagnosis and advanced image processing. Breast healthy is important for women. Breast cancer is the main killer for women. Biomedical breast image data is secondary data. The next process is the initial processing, which is processing that is related to pixel size, gray scale, and so on. The improvement of medical image in this study uses the Mathematical Morphology method which consists of Dilation, Erosion, Opening (Erosion-Dilation) and Closing (Dilation-Erosion) processes. The expected results of this research are medical digital images that have improved their quality as a result of Dilation, Erosion, opening and closing processes.

Clinical Decision Support System in Computational Methods: a Review Study Sri Sumarlinda; AzizahBinti Rahmat; Zalizah Awang Long
Proceeding of International Conference on Science, Health, And Technology Proceeding of the 1st International Conference Health, Science And Technology (ICOHETECH)
Publisher : LPPM Universitas Duta Bangsa Surakarta

Clinical Decision Support Systems (CDSS) are computational models designed impact clinical decision making about individual patients at the point in time that these decision are made. Clinical Decision Support Systems (CDSS) form an important area of research. While traditional systematic literature surveys focus on analyzing literature using arbitrary results, visual surveys allow for the analysis of domains by using complex network-based analytical models. In this paper, we present a detailed visual survey of CDSS literature using important papers selected. The aim of this study is to review a number of articles related to CDSS for heart and stroke diseases. In this study several articles are comparable to the computational methods and rules used for data processing. From the analysis of several sources of literature, the computational methods and rules used in CDSS are Principle Component Analysis (PCA), Support Vector Machine (SVM), Naïve Bayes data mining algorithm, Case Based Recommendation Algorithm, Weighted Fuzzy Rules, Ontology Reasoning, TOPSIS Analysis, Genetic Algorithms, Fuzzy Neural network, Case-based reasoning (CBR), Weighted Fuzzy Rules and Decision Tree.

Clinical Decision Support System for Mapping of Blood Pressure and Heart Rate Sri Sumarlinda; Wiji Lestari
Proceeding of International Conference on Science, Health, And Technology 2021: Proceeding of the 2nd International Conference Health, Science And Technology (ICOHETECH)
Publisher : LPPM Universitas Duta Bangsa Surakarta

Blood pressure has influence on cardiovascular diseases. This study aims to develope clinical decision support system (CDSS) model which non rule based system. The model eas improved using data mining function, especially clustering. K-Means algorithm was used to clustering 120 data and 4 attributes{ age, obesity, sistolic, diastolic and heart rate The clustering process used 500 epoches and 3 cluster. The result of clustering produced 3 cluster. Cluster 1 is higher risk, cluster 2 is medium risk and cluster 3 is normal or lower risk.

Expert System Detecting Symptoms of Game Addiction with The Forward Chaining Method and Certainty Factor Muhammad Mujib Al Khafid; Sri Sumarlinda; Rina Arum Prastyanti
Proceeding of International Conference on Science, Health, And Technology 2021: Proceeding of the 2nd International Conference Health, Science And Technology (ICOHETECH)
Publisher : LPPM Universitas Duta Bangsa Surakarta

Games are fun playing activities. In the past, most children and adolescents played games with physical activities, but nowadays children and adolescents play games with their gadgets. Excessive gaming activity can lead to addiction. Game addiction can cause mental illness, even physical illness. This study aims to help gamers as well as the general public to better understand the symptoms of game addiction and early solutions to game addiction. This study uses forward chaining as a plot, namely by collecting symptoms to find the level of addiction and certainty factors as a calculation by calculating the level of trust and distrust of symptoms. In this study, game addiction resulted in 3 levels, low level game addiction, medium level game addiction and high level game addiction.

Analysis of Non-clinical Risk Factors for Heart Disease and Stroke Using Statistical Method Sri Sumarlinda; Azizah Binti Rahmat; Zalizah Binti Awang Long
Proceeding of International Conference on Science, Health, And Technology Proceeding of the 3rd International Conference Health, Science And Technology (ICOHETECH)
Publisher : LPPM Universitas Duta Bangsa Surakarta

Heart disease and stroke are the main contributions to health disorder. Two factors influence the susceptibility of the disease, namely clinical risk factors and non-clinical factors. This study aims to analyze the effect of non-clinical risk factors on the susceptibility to heart disease and stroke. The non-clinical risk factors are stress management, age, obesity, genetics, smoking, gender, lifestyle (nutrition), lifestyle (timing rest), and physical activity. Analysis of the influence of non-clinical risk factors on susceptibility using statistical methods, namely descriptive statistics, normalization tests with the Kolmogorov-Smirnov test, validity tests, reliability tests, and correlation tests. The descriptive statistical tests show that the risk factors for stress management, obesity, and smoking have a more significant influence than others. While Gender and physical activity have more negligible effect than others. Testing with one sample Kolmogorov-Smirnov Test shows that the data is normally distributed. Validity testing produces 100% valid data. The reliability test using Cronbach's alpha of 9 non-clinical risk factor items resulted in a value of 0.684, which means reliable. Correlation test between 9 items of non-clinical risk factor shows significant between items.

Aplikasi K-Nearest Neighbor (KNN) untuk Klasifikasi Penyakit Kardiovaskuler Sri Sumarlinda; Wiji Lestari
Prosiding Seminar Nasional Teknologi Informasi dan Bisnis Prosiding Seminar Nasional Teknologi Informasi dan Bisnis (SENATIB) 2022
Publisher : Fakultas Ilmu Komputer Universitas Duta Bangsa Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (179.554 KB)

Data mining dan machine learning adalah dua alatyang memainkan peran penting dalam studi analisis data dansistem keputusan. Klasifikasi adalah fungsi dari data mining.Dalam fungsi klasifikasi, pengurutan atau pemetaan terjadiberdasarkan kedekatan atau kesamaan atribut data dengan labelyang ditentukan. Algoritma K-Nearest Neighbor (KNN) adalahmetode non-parametrik yang digunakan untuk klasifikasi danregresi. Model prediksi penyakit kardiovaskular denganalgoritma KNN digunakan untuk mengidentifikasi danmemprediksi penyakit kardiovaskular. Algoritma KNNmenggunakan jarak Euclidian untuk proses prediksi data latih.Dataset yang digunakan adalah 400 dengan 7 atribut yaituumur, jenis kelamin, tekanan darah sistolik, kolesterol, talach,oldpeak dan slope. Hasil implementasi algoritma KNNmenghasilkan performansi dengan akurasi sebesar 75,75%.Nilai presisinya adalah 76,78%, sedangkan recall menghasilkan77,14%.

ALGORITMA K-MEANS CLUSTERING UNTUK SEGMENTASI PELANGGAN (STUDI KASUS : FASHION VIRAL SOLO) Rifal Bayu Ardi; Faulinda Ely Nastiti; Sri Sumarlinda
INFOTECH journal Vol. 9 No. 1 (2023)
Publisher : Universitas Majalengka

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31949/infotech.v9i1.5214

Fashion Viral Solo merupakan salah satu distributor perlengkapan fashion di Surakarta yang belum memanfaatkan data yang dimiliki untuk melakukan segmentasi dan mengelompokkan pelanggan berdasarkan kemiripannya. Segmentasi adalah proses untuk mencari tahu karakteristik dari pelanggan berdasarkan kesamaan tertentu, sehingga memudahkan pengumpulan informasi tentang pelanggan yang memberikan profit bagi perusahaan. Segmentasi menjadi salah satu strategi untuk menghadapi persaingan usaha, untuk mempertahankan pelanggan, dan untuk membantu manajemen dalam menyusun strategi promosi untuk meningkatkan penjualan. Tujuan dari penelitian ini adalah melakukan segmentasi pelanggan yang pernah bertransaksi di Fashion Viral Solo berdasarkan karakteristiknya. Pada penelitian ini, peneliti menggunakan algoritma K-Means untuk melakukan clustering dan penerapan metode Recency, Frequency dan Monetary. K-Means adalah algoritma sederhana, mudah diimplementasikan, tidak lambat, mudah disesuaikan, dan sering digunakan dalam proses data mining khususnya clustering. Nilai atribut Recency menunjukkan waktu terakhir transaksi, Frequency menunjukkan jumlah transaksi, dan Monetary menunjukkan total nominal transaksi. Dengan menggunakan K-Means dan metode Recency, Frequency dan Monetary, peneliti dapat melakukan segmentasi pelanggan Fashion Viral Solo. Dari penelitian ini, diperoleh hasil 2 cluster pelanggan. Cluster 1 dengan jumlah anggota terbanyak, yakni 343 pelanggan dan cluster 2 dengan jumlah anggota 8 pelanggan. Untuk menentukan jumlah cluster yang paling optimal, peneliti menggunakan metode Silhouette Scores dan diperoleh hasil jika membagi pelanggan menjadi 2 cluster adalah yang paling optimal.

Co-Authors Abidarin Rosidi Aditya Prasetyo Wibowo Afu Ichsan Pradana Agustina Srirahayu Andrian Setya Putra Aprilisa Arum Sari Awang Long, Zalizah binti Azizah Binti Rahmat AzizahBinti Rahmat Bintang Pratama Yuarna Saputra binti Rahmat, Azizah Budi Suryono, Arif Daffa Rizki Putra Noordi Dedy Hariyadi Desi Mandasari Dewangga Ranggi Wiku Dwi Hartanti Eko Purwanto endra setiyawan Fadhillah, Dian Agil Faiz Mu’ammar Hadi FAULINDA ELY NASTITI Ghoffar Pangestya Prabowo Hanif Al Fatta Hasanah, Herliyani Humam Rosadi INDRA HASTUTI Indra Hastuti Intan Oktaviani Jayawarsa, A.A. Ketut Joko Endriyanto Junaidi, Iqbal Hanan Khotimah, Agil Husnul Maulana Saputra, Ridlo Maulindar, Joni Moh Muhtarom Muhammad Dhito Maulidan Muhammad Mujib Al Khafid Mumu, Raul Galvin Rudolf Nafaria Rohmandani Nibras Fa'id Muhammad Nibras Fa’id Muhammad Nugroho, Ledjar Nugroho Nugroho, Robby Gusti Nurchim Nurmalitasari Nurmalitasari Oktaviani, Intan Permatasari, Hanifah Pipin Widyaningsih Puput Yulianto Purnama, Joel Adikurnia Ridwan, Avin Nolis Rifal Bayu Ardi Rina Arum Prastyanti Rina Arum Prastyantia Rizqi Saputra Robby Gusti Nugroho Rohmat Eko Prasetyo Ryan Arya Pramudya Saputra, Bintang Pratama Yuarna Saputra, Rizal Dwi Sholeh Abdul Irfan Torodipo Sopingi Sopingi Sopingi Sopingi Sopingi Sopingi, Sopingi Sri Rejeki Supriyanto, Kukuh Tasya Mutiara Diva Udhata Swardana, Avila Vihi Atina Wiji Lestari Wiji Lestari WIJI LESTARI Wiji Lestari Wiji Lestari Wijiyanto Wijiyanto Wijiyanto, Wijiyanto Yeremia Dhimas Abiyoso Yoga Prasetiawan, Ardhian Yuliyanto, Rico Dwi Yuwono, Adhi Zalizah Awang Long Zalizah Binti Awang Long

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