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All Journal Elkom: Jurnal Elektronika dan Komputer Prosiding Seminar Nasional Sains Dan Teknologi Fakultas Teknik Jurnal Pekommas JUITA : Jurnal Informatika Scientific Journal of Informatics Proceeding SENDI_U Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JOURNAL OF APPLIED INFORMATICS AND COMPUTING JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Prosiding Semhavok Journal of Innovation Information Technology and Application (JINITA) Journal of Informatics, Information System, Software Engineering and Applications (INISTA) Jurnal Kajian Ilmiah Jurnal ABDIMAS (Pengabdian kepada Masyarakat) UBJ Jurnal Ilmiah Teknik Mesin, Elektro dan Komputer (JURITEK) DEVOTE: Jurnal Pengabdian Masyarakat Global Indo Green Journal Jurnal INFOTEL Dedikasi: Jurnal Pengabdian Pendidikan dan Teknologi Masyarakat Indonesian Journal of Innovation Science and Knowledge E-Amal: Jurnal Pengabdian Kepada Masyarakat Journal Pesona Indonesia Dedikasi: Jurnal Pengabdian Pendidikan dan Teknologi Masyarakat Indo Green Journal JUILMU
Tri Ginanjar Laksana
Institut Teknologi Telkom Purwokerto
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Medicine & Pharmacology Public Health Social Sciences Other

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SISTEM INFORMASI PEMESANAN ONLINE VILLA GUNUNG GARE MENGGUNAKAN METODE WEB ENGINEERING Laksana, Tri Ginanjar; Chandra, Winoto
SEMHAVOK Vol 2 No 1 (2020): Mei 2020
Publisher : Fakultas Vokasi

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Abstract

Villa Gunung Gare merupakan salah satu penginapan yang berada di Kota Pagar Alam, dimana untuk informasi tentang villa dan proses pemesanan villa gunung gare masih sangat menyulitkan pengunjung. Villa gunung gare juga memiliki fasilitas yang baik tersedia enam villa dimana tiap villa mempunyai tiga kamar ruang tamu dan lainya. Hal ini disebabkan karena pengunjung diharuskan langsung mendatangi villa untuk memperoleh informasi mengenai ketersediaan kamar dan fasilitas villa serta pemesanan villa. Penelitian yang di lakukan untuk membangun sistem online yang di harapkan dapat mengatasi permasalahan yang terjadi di villa gunung gare kota pagar alam khususnya dalam pemesanan kamar. Dalam pelaksanaannya untuk mencapai tujuan tersebut, penelitian ini mengunakan metode penelitian web engineering yang di rasa cocok karena dalam proses pengerjaanya secara berurutan dan juga berbasis web. Kelebihan dari sistem yang baru pada proses pemesanan kamar tamu adalah pengunjung dapat segera mengetahui jumlah kamar yang tersedia dan fasilitas lainnya yang mereka peroleh tanpa harus datang langsung terlebih dahulu ke villa gunung gare. Sistem informasi pemesanan online berbasis web ini dapat digunakan untuk membantu mempermudah dan mengurangi kesalahan khususnya bagian resepsionis dalam mengelola data pemesanan kamar villa serta bermanfaat juga bagi pengunjung yang membutuhkan informasi mengenai lokasi, fasilitas hingga ketersediaan kamar pada Villa Gunung Gare.
PENERAPAN DAN SIMULASI PROTOKOL ROUTING BGP DAN OSPF MENGGUNAKAN METODE REDISTRIBUTE PADA BACKBONE PT. PUSRI Laksana, Tri Ginanjar; Chandra, Winoto
SEMHAVOK Vol 1 No 1 (2019): November 2019
Publisher : Fakultas Vokasi

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Abstract

PT Pupuk Sriwidjaja (PUSRI) adalah salah satu perusahaan yang berstatus Badan Usaha Milik Negara (BUMN). Tiap-tiap pegawai bidang di PT Pupuk Sriwidjaja bekerja menggunakan jaringan dalam penggunaanya, akan tetapi jaringan internet di PT Pupuk Sriwidjaja Palembang memiliki permasalahan kecepatan internet yang belum maksimal dikarenakan perbedaan penerapan routing protokol sehingga mengakibatkan terganggunya performance jaringan komputer di PT.Pupuk Sriwidjaja sehingga menghambat mengirimkan paket-paket data sampai ke tujuan. Salah satu solusinya adalah menggabungkan atau meRedistribute routing protokol yang ada tanpa merombak dari awal sehingga performance sebuah jaringan tersebut tetap terjaga.
PENINGKATAN JUMLAH PENDAFTAR PENERIMAAN SISWA BARU MELALUI PENERAPAN BISNIS INTELEGENT DENGAN TEKNIK OLAP ( ONLINE ANALYCTICAL PROCESS ) Tri Ginanjar Laksana; Bodhi Waluyo Swarna Jakti
Prosiding SNST Fakultas Teknik Vol 1, No 1 (2016): PROSIDING SEMINAR NASIONAL SAINS DAN TEKNOLOGI 7 2016
Publisher : Prosiding SNST Fakultas Teknik

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Abstract

Penerimaan Jumlah pendaftar siswa baru PSB di SMK Islam Annur Losari selama 5 periode terakhir mengalami  pasang surut yang signifikan, dikarenakan daya saing antar sekolah yang semakin tinggi. Keadaan saat ini, pemerintah menerapkan aturan yang sulit dalam meningkatkan kualitas pendidikan di Indonesia dengan kurikulum 2013 ( KURTILAS ) khususnya di wilayah Brebes, Selain hal tersebut, khususnya Sekolah Menengah Kejuruan saling meningkatkan kualitas pendidikan salah satunya adalah pembenahan sarana dan prasarana dalam meningkatkan jumlah pendaftar mahasiswa baru. Penelitian ini akan menggunakan teknik OLAP ( Online Analyctical Processing ) yang didalamnya terdapat tahapan dalam menyelesaikan kasus ini, seperti Analysis Services Project, Integration Service Project, dan Reporting Service Project, Dalam membangun Aplikasi Bisnis Intelijen, Alat bantu yang digunakan adalah POWER OLAP Versi 14, data yang digunakan dari penelitian ini adalah data set ( data warehouse ) penerimaan siswa baru angkatan tahun 2010 – 2015 pada SMK Islam Annur Losari. Penelitian ini diharapakan menghasilkan prediksi atau analisa sementara untuk meningkatkan jumlah pendaftar guna mempermudah pihak SMK Islam Annur Losari  dalam menganalisis perkembangan data Penerimaan Siswa Baru (PSB) dan mengetahui data informasi yang akurat, dan dengan hasil prediksi ini diharapkan pula dapat meningkatkan persentasi jumlah pendaftar penerimaan siswa baru di SMK Islam Annur Losari. Kata kunci: Bisnis Intelijen, KTSP, Kurtilas OLAP, Power OLAP, Warehouse
DIAGNOSIS KEBUTUHAN GIZI PADA BALITA MELALUI PENERAPAN SISTEM PAKAR MENGGUNAKAN METODE CERTAINTY FACTOR Tri Ginanjar Laksana; Elisa Sriyulia
Prosiding SNST Fakultas Teknik Vol 1, No 1 (2016): PROSIDING SEMINAR NASIONAL SAINS DAN TEKNOLOGI 7 2016
Publisher : Prosiding SNST Fakultas Teknik

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Abstract

Keadaaan gizi buruk sudah seharusnya dapat dipantau sedini mungkin, salah satu caranya dengan pemantauan rutin melalui Posyandu dan Puskesmas. Banyak timbul gejala yang mengakibatkan balita mengalami kekurangan gizi yang di akibatkan kurangnya asupan gizi bagi tubuh. Para orangtua balita merasa cemas balitanya mengalami kekurangan gizi karena tidak mengetahui lebih awal gejala yang dialami oleh anak-anaknya. Selain itu gejala yang menyerang pada balita merupakan  resiko tinggi yang harus dihadapi oleh para orangtua. Dalam penelitian ini menggunakan Metode Certainty Factor. Metode ini terdiri dari 3 tahapan diantaranya tahapan pertama menggunakan rule, kedua menentukan nilai bobot, ketiga menghitung nilai Cfnya. Data yang digunakan dari data penyakit yang disebabkan oleh kurangnya asupan gizi dari kebutuhan tubuh. Hasil yang diharapkan mampu melakukan diagnosis dengan cepat, tepat dan akurat terhadap gejala gizi buruk. Selain itu diharapkan mampu membantu para petugas posyandu atau bidan dalam mengantisipasi gejala terjadinya gizi buruk pada balita sejak dini sehingga langkah pencegahan segera di lakukan. Diperlukan keakuratan dan ketepatan dalam mendiagnosis gejala penyakit guna menyimpulkan hasil yang di harapkan. Kata kunci : Certainty Factor, Diagnosis, Gizi Buruk, Sistem Pakar, SDM
Combination of Support Vector Machine and Lexicon-Based Algorithm in Twitter Sentiment Analysis Rindu Hafil Muhammadi; Tri Ginanjar Laksana; Amalia Beladinna Arifa
Khazanah Informatika Vol. 8 No. 1 April 2022
Publisher : Department of Informatics, Universitas Muhammadiyah Surakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/khif.v8i1.15213

Abstract

Data from the Ministry of Civil Works and Public Housing (Kementrian PUPR) in 2019 shows that around 81 million millennials do not own houses. Government Regulation Number 25 of 2020 on the Implementation of Public Housing Savings, commonly called PP 25 Tapera 2020, is one of the government's efforts to ensure that Indonesian people can afford houses. Tapera is a deposit of workers for house financing, which is refundable after the term expires. Immediately after enaction, there were many public responses regarding the ordinance. We investigate public sentiments commenting on the regulation and use Support Vector Machine (SVM) in the study since it has a good level of accuracy. It also requires labels and training data. To speed up labeling, we use the lexicon-based method. The issue in the lexicon-based lies in the dictionary component as the most significant factor. Therefore, it is possible to update the dictionary automatically by combining lexicon-based and SVM. The SVM approach can contribute to lexicon-based, and lexicon-based can help label datasets on SVM to produce good accuracy. The research begins with collecting data from Twitter, preprocessing raw and unstructured data into ready-to-use data, labeling the data with lexicon-based, weighting with TF-IDF, processing using SVM, and evaluating algorithm performance model with a confusion matrix. The results showed that the combination of lexicon-based and SVM worked well. Lexicon-based managed to label 519 tweet data. SVM managed to get an accuracy value of 81.73% with the RBF kernel function. Another test with a Sigmoid kernel attains the highest precision at 78.68%. The RBF kernel has the highest recall result with a value of 81.73%. Then, the F1-score for both the RBF kernel and Sigmoid is 79.60%.
Optimasi Akurasi Metode Convolutional Neural Network untuk Identifikasi Jenis Sampah Rima Dias Ramadhani; Afandi Nur Aziz Thohari; Condro Kartiko; Apri Junaidi; Tri Ginanjar Laksana; Novanda Alim Setya Nugraha
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 5 No 2 (2021): April 2021
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (417.185 KB) | DOI: 10.29207/resti.v5i2.2754

Abstract

Waste is goods / materials that have no value in the scope of production, where in some cases the waste is disposed of carelessly and can damage the environment. The Indonesian government in 2019 recorded waste reaching 66-67 million tons, which is higher than the previous year, which was 64 million tons. Waste is differentiated based on its type, namely organic and anorganic waste. In the field of computer science, the process of sensing the type waste can be done using a camera and the Convolutional Neural Networks (CNN) method, which is a type of neural network that works by receiving input in the form of images. The input will be trained using CNN architecture so that it will produce output that can recognize the object being inputted. This study optimizes the use of the CNN method to obtain accurate results in identifying types of waste. Optimization is done by adding several hyperparameters to the CNN architecture. By adding hyperparameters, the accuracy value is 91.2%. Meanwhile, if the hyperparameter is not used, the accuracy value is only 67.6%. There are three hyperparameters used to increase the accuracy value of the model. They are dropout, padding, and stride. 20% increase in dropout to increase training overfit. Whereas padding and stride are used to speed up the model training process.
Prediksi Penyakit Ginjal Kronis Menggunakan Hibrid Jaringan Saraf Tiruan Backpropagation dengan Particle Swarm Optimization Sheren Afryan Tiastama; Tri Ginanjar Laksana; Amalia Beladinna Arifa
Journal of Innovation Information Technology and Application (JINITA) Vol 3 No 1 (2021): JINITA, June 2021
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (371.287 KB) | DOI: 10.35970/jinita.v3i1.588

Abstract

The number of Chronic Kidney Disease patient increased year by year while it doesn’t following by sufficient human resources and infrastructure needs the information of Chronic Kidney Disease patient prediction. Prediction of Chronic Kidney Disease patient is necessary to be done as an anticipation for preparing the better human resources and infrastructure that will effect to patient survival rate. In this study, backpropagation artificial neural network and particle swarm optimization combination used to predict the number of Chronic Kidney Disease patient. Artificial Neural Network has the ability in time series data prediction, such as the number of Chronic Kidney Disease year by year. But, backpropagation artificial neural network has a weakness in weight inisialization which taken unoptimally that could cause bad convergence speed. Particle swarm optimization will resolve the backpropagation artificial neural network weakness by weights optimization that will used in backpropagation artificial neural network. The Artificial Neural Network and Particle Swarm Optimization have several parameters, such as the number of hidden layer neuron, learning rate, and swarm. This research is using RSUD Banyumas Chronic Kidney Disease patient data in 2011 until 2020. Matlab R2019a used in this research as a software to predict chronic kidney disease patient data. The test results shows the prediction accuracy based on Mean Squared Error value is 0,0370 using 12-16-1 artificial neural network architecture, 0.005 learning rate, 1250 epochs and 50 swarms
Kombinasi Single Linkage Dengan K-Means Clustering Untuk Pengelompokan Wilayah Desa Kabupaten Pemalang Sintiya; Tri Ginanjar Laksana; Nia Annisa Ferani Tanjung
Journal of Innovation Information Technology and Application (JINITA) Vol 3 No 1 (2021): JINITA, June 2021
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (675.416 KB) | DOI: 10.35970/jinita.v3i1.589

Abstract

K-Means is very dependent on determining the center cluster initial which has an impact on the quality of clusters resulting, in addition to determining the center of cluster the number of k that will be used it can also affect the quality of the cluster from the method K-Means. Poverty is mostly experienced by rural communities, this can be seen from the lack of existing facilities to serve the interests of the community in various fields. To avoid the imbalance that occurs, a development plan is needed in accordance with the characteristics of the welfare of the people in the region. Therefore, we need an effort to group villages so that policy making is right on target. One of the algorithms clustering that is commonly used is the K-Means algorithm because it is quite simple, easy to implement, and has the ability to group large data groups very quickly. However, the K-Means algorithm has a weakness in determining the center cluster initial given. Initialization of centers cluster randomly may result in formation clusters changing (inconsistent). For this reason, the K-Means method needs to be combined with the hierarchical method in determining the center cluster initial. This combination method is called Hierarchical K-Means which is a combination of methods hierarchical and partitioning, where the process is hierarchical used to find the initial center initialization cluster and the process partitioning to get the cluster optimal. The hierarchical method used in this study is the method single linkage. Based on the method Elbow , the recommended amount of k for this study is k = 4.The combination of the single linkage and k-means algorithms with k = 4 in this study results in avalue silhouette coefficient of 0.685 which is a feasible or appropriate cluster category, while the evaluation measurement by Davies The Boulldin Index yielded a value of 0.577.
Analisis Pengendalian Kualitas Produksi Ikan Dengan Metode Six Sigma Untuk Mengurangi Jumlah Cacat Produk Nila Aulia Musa; Tri Ginanjar Laksana; Nia Annisa Ferani Tanjung
Jurnal Ilmiah Teknik Mesin, Elektro dan Komputer Vol 2 No 1 (2022): Maret, Jurnal Ilmiah Teknik Mesin, Elektro dan Komputer
Publisher : Sekolah Tinggi Ilmu Ekonomi Trianandra

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/juritek.v2i1.852

Abstract

PT Patria Perikanan Lestari Indonesia Penelitian ini bertujuan untuk memecahkan masalah yang berkaitan dengan pengendalian Kualitas produk dengan Metode Six Sigma dan memberikan solusi dengan implementasi perbaikan untuk proses produksi yang ada pada perusahaan. Metode penelitian ini adalah metode Six Sigma, metode yang merupakan pengendali kualitas produksi. Metode analisis Six Sigma digunakan tahap DMAIC untuk tahapan pemecahan masalah yang ada pada perushaan. Dengan menggunakan tolls yang ada pada seven tolls yang merupakan alat pengendali kualitas. Berdasarkan pengolahan data diketahui dengan adanya penerapan six sigma pada permasalahan. Diketahui hasil dari pengolahan data yaitu untuk jumlah produk cacat dari tiga jenis produk yang di produksi oleh perusahaan produk jenis loin tuna memilik presentasi cacat paling besar dibandingkan dengan produk tuna cube dan tuna fillet, dengan jumlah cacat pada bulan juni 2020 sampai mei 2021 sebesar 1017.75 kg, dengan nilai presentasi jumlah cacat 162.2 Nilai Sigma yang diperoleh adalah sebesar 4.1416.
Combination of Backpropagation Neural Network and Particle Swarm Optimization for Water Production Prediction in Municipal Waterworks Agustyawan, Arif; Laksana, Tri Ginanjar; Athiyah, Ummi
Scientific Journal of Informatics Vol 9, No 1 (2022): May 2022
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v9i1.29849

Abstract

Abstract.Purpose: As the population grows, the need for clean water also increases. Municipal Waterworks (PDAM) is an institution that regulates and manages the procurement of clean water for the community. So, the amount of water produced and distributed should be adjusted to the demand for water. Predictions on PDAM water production need to be done as planning and better preparation and facilitating and assisting in decision-making.Methods: The study used the Neural Network backpropagation algorithm combined with Particle Swarm Optimization (PSO) to predict the amount of water PDAM should produce. Backpropagation has a good ability to make predictions. But backpropagation has a weakness that causes it to get stuck at a local minimum. This is influenced by the determination of weights that are not optimal. In this study, PSO had a role in optimizing error values on the network to gain optimal weight. Result: This study obtained MSE values in the training and testing process of 0.00179 and 0.00081 from the combination model of backpropagation ANN and PSO. It is smaller than the ANN model without using an optimization algorithm.Novelty: The combination of JST backpropagation and PSO can improve predictions' accuracy and produce optimum weights.
Co-Authors Adam Rifais Ade Rahmat Iskandar Afandi Nur Aziz Thohari Agripina, Ailsa Agustyawan, Arif Ahmad, Wan Nooraishya Wan Akbar Fadhillah, Maulana Amalia Beladinna Arifa Andi Setiawan Apri Junaidi Arimbi Dwimita Putri, Devina Aryanto, Didit Aulya Yunitasari, Dwi Azhar, Mochammad Fikry Maulana Bodhi Waluyo Swarna Jakti Budi, Rifky Kurniawan Setya Chandra, Winoto Condro Kartiko Deni Prasetyo, Deni Dian Nurdiansyah Dwi Nugroho, Alfian Ekantoro, Julyanto Elisa Sriyulia Enny Istanti Faqih, Abdul Haris Fauzan Firaus, Azka Fauziah, Nina Syifa Febriati, Atik Iwan Abadi JUBAEDAH JUBAEDAH, JUBAEDAH Jubaedah, Jubaedah Kurnia Budiawan, Nadjib Kurnia, Dian Ade Kurnia, Dian Ade Martin Parhusip Maulana Syahputra, Aditya Maulana Wibowo, Ariel Maulana, Nur Muhamad, Pagi Muhammad Zidny Naf'an Nabila, Farah Nada Kusnendar, Ahmad Nansa Anwar, Ulis Nia Annisa Ferani Tanjung Nila Aulia Musa Nining, Sri Novanda Alim Setya Nugraha Novandra, Gagas Nur Baitti, Silvi Nuraini, Karina Nuri Annisa Nurun Ala, Hindun Pranoto, Imam Aji Prasetyo, Aditya Budi Pratama, Muhammad Iqbal Pujiono, Krisna Dimas Purnama, Rafi Putri Ilhami, Ginaris Rahayu, Sintia Rahwi, Muhamad Alif Ramadhan, Alfajri Ramadhani, Rima Dias Rasmila Rasmila, Rasmila Rindu Hafil Muhammadi Rizky, Miftahur Rofi Ariansyah, Muhammad Sabil, Muhamad Sauri, Yayan Sofyan Setiadi, Muhamad Irfan Sheren Afryan Tiastama Simamora, Muhammad Gifari H Sintiya Siti Khomsah, Siti Suarna, Nana Suarna, Nana Sudianto Syahrani, Gina Syaputra, Darmawan Rizky Ummi Athiyah Utama, Rizki Bintang Utama, Rizki Bintang Utami, Annisaa Wibowo, Ariel Maulana Widi Afandi Yanuardi Leksono, Fatur Zakaria, Muhammad Faridzain