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<![CDATA[PENERAPAN ALGORITMA PRIM DAN KRUSKAL PADA JARINGAN DISTRIBUSI AIR PDAM TIRTA MOEDAL CABANG SEMARANG UTARA ]]>
<![CDATA[Latifah, Umi]]>;
<![CDATA[Sugiharti, Endang]]>
<![CDATA[ Unnes Journal of Mathematics Vol 4 No 1 (2015) ]]>
Publisher : <![CDATA[Universitas Negeri Semarang ]]>
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DOI: 10.15294/ujm.v4i1.7418
<![CDATA[Algoritma Prim dan Kruskal adalah algoritma yang dapat digunakan untuk mencari pohon rentang minimum untuk graf berbobot. Permasalahan dalam penulisan skripsi ini adalah bagaimana hasil pohon rentang minimum menggunakan algoritma Prim dan Kruskal, serta bagaimana aplikasinya menggunakan MATLAB. Dari data yang diperoleh dapat disusun gambar jaringan. Selanjutnya dari gambar jaringan dapat diperoleh pohon rentang minimum menggunakan algoritma Prim dan Kruskal, dengan bantuan program MATLAB. Berdasarkan hasil penelitian dan pembahasan dapat disimpulkan bahwa pohon rentang minimum dari A1 (PDAM) ke A51 (titik penyambungan pipa) menggunakan algoritma Prim dan program MATLAB adalah 24.365 m. Begitupula menggunakan algoritma Kruskal dan program MATLAB ternyata 24.365 m. Hal ini mengakibatkan penghematan pipa pendistribusian sepanjang 12.735 m dari panjang total sebelumnya yaitu 37.100 m.]]>
<![CDATA[IMPLEMENTASI ALGORITMA GENETIKA DENGAN TEKNIK KENDALI LOGIKA FUZZY UNTUK MENGATASI TRAVELLING SALESMAN PROBLEM MENGGUNAKAN MATLAB ]]>
<![CDATA[Fitriana, Erma Nurul]]>;
<![CDATA[Sugiharti, Endang]]>
<![CDATA[ Unnes Journal of Mathematics Vol 4 No 2 (2015) ]]>
Publisher : <![CDATA[Universitas Negeri Semarang ]]>
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DOI: 10.15294/ujm.v4i2.9352
<![CDATA[Algoritma Genetika dengan Teknik Kendali Logika Fuzzy adalah algoritma yang dapat digunakan untuk mengatasi Travelling Salesman Problem. Permasalahan dalam penulisan skripsi ini adalah bagaimana hasil jarak minimum dari jaringan TSP menggunakan algoritma genetika dengan teknik kendali logika fuzzy, serta bagaimana aplikasinya menggunakan MATLAB. Dari data yang diperoleh dapat ditentukan koordinat. Selanjutnya dari koordinat dapat diperoleh solusi optimal dengan menggunakan masukan populasi dan generasi tertentu dengan bantuan software MATLAB. Dari hasil analisis algoritma genetika dengan teknik kendali logika fuzzy diperoleh hasil bahwa solusi optimal menggunakan masukkan populasi 100 dan generasi 1000 lebih baik dari solusi optimal yang didapatkan dengan masukkan populasi dan generasinya berturut-turut adalah (100 dan 100), (100 dan 200), (100 dan 500), (200 dan 100), (500 dan 100) dan (1000 dan 100). Kemudian didapatkan rute terbaiknya adalah 1-3-4-6-9-8-7-19-18-16-17-20-21-22-15-12-11-10-14-13-5-2-1 dan panjang jalur terbaiknya adalah 22,63 Km]]>
<![CDATA[Aplikasi Mobile Sistem Informasi Akademik Labschool Universitas Negeri Semarang Berbasis Android ]]>
<![CDATA[Hariyanto, Abdul]]>;
<![CDATA[Sugiharti, Endang]]>;
<![CDATA[Arifudin, Riza]]>
<![CDATA[ Unnes Journal of Mathematics Vol 8 No 1 (2019) ]]>
Publisher : <![CDATA[Universitas Negeri Semarang ]]>
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DOI: 10.15294/ujm.v8i1.13915
<![CDATA[Kemajuan teknologi yang sangat pesat terutama teknologi mobile, sudah memaksa segala bidang untuk mengikuti perkembangan teknologi yang sudah ada. Salah satunya adalah bidang pendidikan. Labschool Universitas Negeri Semarang telah menerapkan teknologi informasi pada sistem pembelajarannya. Teknologi informasi yang telah dikembangkan adalah SIAKAL (Sistem Informasi Akademik Labschool). Masalah selanjutnya adalah ketika perkembangan teknologi mobile sudah jauh berkembang menjadikan SIAKAL susah untuk dibuka dengan perangkat mobile, karena tampilan masih belum responsive. Dalam penelitian ini, peneliti akan merancang dan membangun aplikasi mobile sistem informasi akademik Labschool berbasis android. Aplikasi dikembangkan dengan menggunakan metode waterfall, dengan proses analisa sistem, desain sistem, pembuatan sistem, pengujian, dan pemeliharaan. Pengujian aplikasi dilakukan dengan metode Blackbox dan pengujian oleh user. Hasil akhir penelitian diketahui bahwa aplikasi dapat terimplementasi dengan baik pada perangkat android dengan versi 4.2 Jelly Bean sampai 4.4 Kitkat yang memiliki ukuran layar yang berbeda.]]>
<![CDATA[Sistem informasi administrasi pengelolaan iuran bulanan berbasis SMS gateway dengan menggunakan model perangkat lunak prototype ]]>
<![CDATA[Adha, Nugraha Saputra]]>;
<![CDATA[Sugiharti, Endang]]>;
<![CDATA[Arifudin, Riza]]>
<![CDATA[ Unnes Journal of Mathematics Vol 7 No 2 (2018) ]]>
Publisher : <![CDATA[Universitas Negeri Semarang ]]>
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DOI: 10.15294/ujm.v7i2.14165
<![CDATA[Tujuan dari penelitian ini adalah untuk membuat sistem informasi administrasi pengelola iuran bulanan berbasis SMS Gateway yang dapat merekap setiap transaksi iuran bulanan sekolah serta mengirimkan SMS kepada wali murid sebagai bukti pembayaran serta wali murid dapat mengirimkan SMS kepada sistem untuk memeriksa pembayaran iuran bulanan putra/putrinya. Dalam pembuatan sistem informasi administrasi ini menggunakan software Visual Basic .Net dan MySQL sebagai pengolah database. Selain itu, model pengembangan perangkat lunak Prototype juga digunakan untuk mempermudah proses pengembangan aplikasi ini. Data yang digunakan dalam penelitian ini diperoleh berdasakan observasi lapangan di sekolah MA-Al Ishlah. Data tersebut digunakan untuk membentuk struktur database agar sistem berjalan dengan baik sesuai dengan kebutuhan. Berdasarkan hasil penenlitian, diperoleh sebuah aplikasi yang mampu membantu petugas tata usaha dalam mengelola administrasi keuangan sekolah. Pada program ini petugas tata usaha dapat melakukan pengelolaan transaksi pembayaran, membuat laporan transaksi baik laporan pembayaran maupun laporan tunggakan. The purpose of this research is to create administrative information system management of monthly fee based on SMS Gateway which can reconcile every transaction of monthly fee and send SMS to parents as proof of payment and parents can send SMS to system to check payment of monthly fee of their son/their daughter. To create this administrative information system using Visual Basic.Net software and MySQL as a database processor. In addition, Prototype software development model is also used to simplify the process of developing this application.The data used in this research is obtained based on field observation at MA-Al Ishlah school. The data is used to create the database structure for the system to run properly in accordance with the needs. Based on the results of this research, obtained an application that is able to help administrative officers in managing school financial administration. In this program administrative officers can manage payment transactions, making transaction reports both payment reports and arrears reports.]]>
<![CDATA[EFEKTIVITAS ALGORITMA CLARKE-WRIGHT DAN SEQUENTIAL INSERTION DALAM PENENTUAN RUTE PENDISTRIBUSIAN TABUNG GAS LPG ]]>
<![CDATA[Rupiah, Siti]]>;
<![CDATA[Mulyono, Mulyono]]>;
<![CDATA[Sugiharti, Endang]]>
<![CDATA[ Unnes Journal of Mathematics Vol 6 No 2 (2017) ]]>
Publisher : <![CDATA[Universitas Negeri Semarang ]]>
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DOI: 10.15294/ujm.v6i2.20484
<![CDATA[Permasalahan distribusi tabung gas LPG dari salah satu agen LPG di Blora yaitu PT. X ke beberapa sub agen/pangkalan merupakan contoh kasus permasalahan Capacitated Vehicle Routing Problem (CVRP). Permasalahan dalam penelitian ini adalah bagaimana menyelesaikan masalah rute pendistribusian tabung gas LPG menggunakan algoritma Clarke-Wright dan algoritma Sequential Insertion. Pencarian rute tersebut dilakukan secara hitungan manual dan dengan bantuan program Matlab R2014a. Selanjutnya akan ditentukan keefektifan dari penggunaan kedua algoritma tersebut. Pengambilan data dilakukan dengan metode observasi dan wawancara secara langsung dengan pegawai di PT. X. Simpulan yang diperoleh adalah pada solusi algoritma Clarke-Wright diperoleh penghematan jarak sebesar 146,2 km/minggu dan penghematan biaya transportasi sebesar Rp94.116,25/minggu; Sedangkan pada solusi algoritma Sequential Insertion diperoleh penghematan jarak sebesar 160,2 km/minggu dan penghematan biaya transportasi sebesar Rp103.128,75/minggu. Dengan demikian dapat disimpulkan bahwa rute yang dibentuk menggunakan algoritma Sequential Insertion pada kasus ini lebih efektif dibandingkan rute yang dibentuk menggunakan algoritma Clarke-Wright.]]>
<![CDATA[Integration of convolutional neural network and extreme gradient boosting for breast cancer detection ]]>
<![CDATA[Endang Sugiharti]]>;
<![CDATA[Riza Arifudin]]>;
<![CDATA[Dian Tri Wiyanti]]>;
<![CDATA[Arief Broto Susilo]]>
<![CDATA[ Bulletin of Electrical Engineering and Informatics Vol 11, No 2: April 2022 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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DOI: 10.11591/eei.v11i2.3562
<![CDATA[With the most recent advances in technology, computer programming has reached the capabilities of human brain to decide things for almost all healthcare systems. The implementation of Convolutional Neural Network (CNN) and Extreme Gradient Boosting (XGBoost) is expected to improve the accurateness of breast cancer detection. The aims of this research were to; i) determine the stages of CNN-XGBoost integration in diagnosis of breast cancer and ii) calculate the accuracy of the CNN-XGBoost integration in breast cancer detection. By combining transfer learning and data augmentation, CNN with XGBoost as a classifier was used. After acquiring accuracy results through transfer learning, this reasearch connects the final layer to the XGBoost classifier. Furthermore, the interface design for the evaluation process was established using the Python programming language and the Django platform. The results: i) the stages of CNN-XGBoost integration on histopathology images for breast cancer detection were discovered. ii) Achieved a higher level of accuracy as a result of the CNN-XGBoost integration for breast cancer detection. In conclusion, breast cancer detection was revealed through the integration of CNN-XGBoost through histopathological images. The combination of CNN and XGBoost can enhance the accuracy of breast cancer detection.]]>
<![CDATA[Implementation of Discretisation and Correlation-based Feature Selection to Optimize Support Vector Machine in Diagnosis of Chronic Kidney Disease ]]>
<![CDATA[Dwika Ananda Agustina Pertiwi]]>;
<![CDATA[Pipit Riski Setyorini]]>;
<![CDATA[Much Aziz Muslim]]>;
<![CDATA[Endang Sugiharti]]>
<![CDATA[ Buletin Ilmiah Sarjana Teknik Elektro Vol. 5 No. 2 (2023): June ]]>
Publisher : <![CDATA[Universitas Ahmad Dahlan ]]>
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DOI: 10.12928/biste.v5i2.7548
<![CDATA[This study aims to improve the accuracy of the classification algorithm for diagnosing chronic kidney disease. There are several models of data mining. In classification, the Support Vector Machine (SVM) algorithm is widely used by researchers worldwide. The data used is a chronic kidney disease dataset taken from the UCI machine learning repository. This data consists of 25 attributes and 11 numeric data attributes, and 14 negative attributes. To call continuously, discrete data is used. Meanwhile, data is selected using Correlation-based Feature Selection (CFS) to reduce irrelevant and redundant data. The research results by applying discretization and feature selection based on correlation for classification in the SVM algorithm with 10-fold cross-validation show an increase in accuracy of 0.5%. The classification of the vector machine support algorithm in the diagnosis of chronic kidney disease produces an accuracy of 99.25%, and after applying discretization and correlation-based feature selection, produces an accuracy of 99.75%. Implementation of discretion and correlation-based feature selection to optimize support vector machine for diagnosis of chronic kidney disease has increased accuracy by 0.5%. The proposed method is feasible as a method of diagnosing chronic kidney disease.]]>
<![CDATA[MENINGKATKAN KEMAMPUAN MEMECAHKAN MASALAH BAGI MAHASISWA PGMIPABI DALAM PERKULIAHAN TELKURMAT-2 MELALUI PENERAPANMIND-MAPPING BERCIRI KONSERVASI ]]>
<![CDATA[Amin Suyitno]]>;
<![CDATA[Endang Sugiharti]]>
<![CDATA[ Jurnal Penelitian Pendidikan Vol 30, No 1 (2013): April 2013 ]]>
Publisher : <![CDATA[Universitas Negeri Semarang ]]>
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DOI: 10.15294/jpp.v30i1.5661
<![CDATA[Ability to solve problems for students of PGMIPABI Programin Analysis of Mathematics Curriculum 2 stillneeds to be improved.One way of it istoimplementof Mind Mapping based-on Conservationthattrain studentsfor independent study, creatitive, and get to knowthe environment by themselves.The problem ishow to improveproblem-solving abilityfor studentsof PGMIPABIof MathematicsEducation Study Program of UNNES, especiallyin the Analysis of Mathematics Curriculum 2 lecturing. The purposeof this researchis to improve problem-solving abilityfor studentsof PGMIPABIof MathematicsEducation Study Program of UNNES, especiallyin the Analysis of Mathematics Curriculum 2 lecturing.The resultsand conclusionsare asfollows.By applyingof Mind Mapping based-onConservation thenproblem-solving skillsfor studentsof PGMIPABIof MathematicsEducation Study Program of UNNES, especiallyin the Analysis of Mathematics Curriculum 2 lecturing canbe increased. The averagescoreobtained bystudentswas85.6,the averagescorewas higherthanthe averagescore ofthe previous years.The suggestions are (1)development ofapplyingof Mind Mapping based-on Conservation to improve problem-solving abilityfor studentsof PGMIPABIof MathematicsEducation Study Program of UNNES, especiallyin the Analysis of Mathematics Curriculum 2 shouldbe followed. (2)Needafurther research tothe othersubject and learning model base-on conservation.]]>
<![CDATA[Hyperparameter Optimization Using Hyperband in Convolutional Neural Network for Image Classification of Indonesian Snacks ]]>
<![CDATA[Asyrofiyyah, Nuril]]>;
<![CDATA[Sugiharti, Endang]]>
<![CDATA[ Recursive Journal of Informatics Vol 2 No 1 (2024): March 2024 ]]>
Publisher : <![CDATA[Universitas Negeri Semarang ]]>
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DOI: 10.15294/rji.v2i1.72720
<![CDATA[Abstract. Indonesia is known for its traditional food both domestically and abroad. Several cakes are included in favorite traditional foods. Of the many types of cakes that exist, it is visually easy to recognize by humans, but computer vision requires special techniques in identifying image objects to types of cakes. Therefore, to recognize objects in the form of images of cakes as one of Indonesian specialties, a deep learning algorithm technique, namely the Convolutional Neural Network (CNN) can be used. Purpose: This study aims to find out how the Convolutional Neural Network (CNN) works by optimizing the hyperband hyperparameter in the classification process and knowing the accuracy value when hyperband is applied to the optimal hyperparameter selection process for classifying Indonesian snack images. Methods/Study design/approach: This study optimizes the hyperparameter Convolutional Neural Network (CNN) using Hyperband on the Indonesian cake dataset. The dataset is 1845 images of Indonesian snacks which consists of 1523 training data, 162 validation data and 160 testing data with 8 classes. In training data, the dataset is divided by 82% on training data, 9% validation, and 9% testing. Result/Findings: The best hyperparameter value produced is 480 for the number of dense neurons 2 and 0.0001 for the learning rate. The proposed method succeeded in achieving a training value of 87.53%, for the validation process it was obtained 66.8%, the testing process was obtained 79.37%. Results obtained from model training of 50 epochs. Novelty/Originality/Value: Previous research focused on the application and development of algorithms for the classification of Indonesian snacks. Therefore, optimizing hyperparameters in a Convolutional Neural Network (CNN) using Hyperband can be an alternative in selecting the optimal architecture and hyperparameters.]]>
<![CDATA[Implementation of Random Forest with Synthetic Minority Oversampling Technique and Particle Swarm Optimization for Predicting Survival of Heart Failure Patients ]]>
<![CDATA[Zaaidatunni'mah, Untsa]]>;
<![CDATA[Sugiharti, Endang]]>
<![CDATA[ Recursive Journal of Informatics Vol 2 No 2 (2024): September 2024 ]]>
Publisher : <![CDATA[Universitas Negeri Semarang ]]>
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DOI: 10.15294/rji.v2i2.76142
<![CDATA[Abstract. Heart failure is caused by a disruption in the heart’s muscle wall, which results in the heart’s inability to pump blood in sufficient quantities to meet the body’s demand for blood. The increasing prevalence and mortality rates of heart failure can be reduced through early disease detection using data mining processes. Data mining is believed to aid in discovering and interpreting specific patterns in decision-making based on processed information. Data mining has also been applied in various fields, one of which is the healthcare sector. One of the data mining techniques used to predict a decision is the classification technique. Purpose: This research aims to apply SMOTE and PSO to the Random Forest classification algorithm in predicting the survival of heart failure patients and to determine its accuracy results. Methods/Study design/approach: To predict the survival of heart failure patients, we utilize the Random Forest classification algorithm and incorporate data imbalance handling with SMOTE and feature selection techniques with PSO on the Heart Failure Clinical Records Dataset. The data mining process consists of three distinct phases. Result/Findings: The application of SMOTE and PSO on the Heart Failure Clinical Records Dataset in the Random Forest classification process resulted in an accuracy rate of 93.9%. In contrast, the Random Forest classification process without SMOTE and PSO resulted in an accuracy rate of only 88.33%. This indicates that the proposed method combination can optimize the performance of the classification algorithm, achieving a higher accuracy compared to previous research. Novelty/Originality/Value: Data imbalance and irrelevant features in the Heart Failure Clinical Records Dataset significantly impact the classification process. Therefore, this research utilizes SMOTE as a data balancing method and PSO as a feature selection technique in the Heart Failure Clinical Records Dataset before the classification process of the Random Forest algorithm.]]>
