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All Journal Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Journal of Innovation Information Technology and Application (JINITA) E-Amal: Jurnal Pengabdian Kepada Masyarakat Jurnal Ilmiah Teknik Mesin, Elektro dan Komputer (JURITEK) DEVOTE: Jurnal Pengabdian Masyarakat Global
laksana, tri ginanjar
Institut Teknologi Telkom Purwokerto
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 Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Public Health Social Sciences Other

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DETEKSI KERUSAKAN HANDPHONE SAMSUNG MELALUI SISTEM PAKAR MENGGUNAKAN KOMBINASI ALGORITMA K-NEAREST NEIGHBOR DENGAN CASE BASED REASONING laksana, tri ginanjar
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 4, No 1 (2019)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (514.727 KB) | DOI: 10.29100/jipi.v4i1.1031

Abstract

Berdasaarkan hasil observasi yang dilakukan dilapangan, diperoleh bahwa kurangnya tenaga ahli yang dapat memperbaiki kerusakan pada handphone terutama handphone samsung dan banyaknya masyarakat awam yang kurang mengetahui gejala-gejala kerusakan handphone samsung, maka perlu adanya suatu teknologi informasi yang dapat membantu masyarakat awam dalam mengatasi kerusakan handphone tersebut.dalam penelitian ini menggunakan pendekatan sistem pakar dengan metode Kombinasi Algoritma K-Nearest Neighbor  Dengan Case Based Reasoning, menggunakan bahasa pemrograman Visual Studio. Adapun tujuan penelitian ini yaitu : membuat sistem pakar untuk mengidentifikasi kerusakan dan solusi perbaikan handphone Samsung dengan kombinasi metode Case Based Reasoning dan algoritma K Nearest Neighbo, mnunjukkan tingkat akurasi dari penerapan metode Case Based Reasoning dengan kombinasi algoritma K Nearest Neighbor pada sistem pakar deteksi kerusakan handphone Samsung. Hasil dari penelitian ini masyarakat awam dapat mengetahui jenis kerusakan handphone Samsung tanpa harus berkunjung ke service cente, mengetahui seberapa besar tingkat akurasi yang dihasilkan dari kombinasi metode Case Based Reasoning dan K-Nearest Neighbor.
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
PENINGKATAN PEMASARAN PRODUK YANG LESS CONTACT DI DESA WISATA ADILUHUR MELALUI PEMANFAATAN TEKNOLOGI INFORMASI DIMASA PANDEMI COVID - 19 Tri Ginanjar Laksana; Dian Nurdiansyah; Novanda Alim Setya Nugraha; Rima Dias Ramadhani; Siti Khomsah
E-Amal: Jurnal Pengabdian Kepada Masyarakat Vol 2 No 2: Mei 2022
Publisher : LP2M STP Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Data yang telah dipublikasi oleh Kementrian kesehatan Republik Indonesia tahun 2021, terkait perkembangan virus COVID-19, hingga saat menjadi masalah utama bagi kesehatan masyarakat, khususnya bagi perekonomian di Indonesia. Desa Wisata Adiluhur memiliki beragam desa wisata sehingga dapat menggerakkan perekonomian warga desa. Masyarakat sudah mulai sadar dan ikut terlibat langsung dengan aktivitas pariwisata, dimana mereka memberdayakan diri mereka di berbagai kegiatan ekonomi pariwisata seperti menyewakan homestay, berjualan makanan tradisional, dan membuat kerajinan tangan. Produk-produk hasil penduduk di Desa Adilihur masih dijual secara offline sehingga untuk mendapatkan produk dari desa wisata, masyarakat harus datang langsung. Begitu pula dengan paket wisata museum dan agrowisata belum bisa dibeli secara online. Pengunjung desa wisata Adilihur paling banyak dari daerah Kebumen saja. Tingkat kunjungan jauh menurun ditambah pengelola belum mampu menerapkan standar CHSE atau Cleanliness (Kebersihan), Health (Kesehatan), Safety (Keamanan), dan Environment Sustainability (Kelestarian Lingkungan). Metode pengabdaian masyarakat ini menggunakan penelitian kualitatif dan pendekatan yang digunakan dalam pengabdilan masyarakat ini adalah Community Action Model (CAM), pengabdian masyarakat ini dilakukan dengan cara blended learning antara luring dan daring menggunakan zoom meeting. Program pengabdian masyarakat ini dibuka oleh kepala desa adiluhur dan disambut baik oleh kelompok Desa wisata di adiluhur. Hasil pengabdian masyarakat ini menunjukkan bahwa kegiatan pengabdian masyarakat terkait penerapan teknologi informasi dalam pemasaran produk yang less contact ini mampu meningkatkan pemasaran dan penghasilan kelompok desa wisata, dimana sebelumnya awam/tidak tahu terhadap teknologi informasi. Melalui pengabdian masyarakat ini kelompok desa wisata dapat turut aktif dalam pemasaran melalui internet dalam pemasaran produk dengan less contact di desa adiluhur.
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.
PEMBERDAYAAN DAN PENINGKATAN KAPASITAS KELEMBAGAAN MASYARAKAT DESA MELALUI AGROWISATA BERBAHASA INGGRIS Novanda Alim Setya Nugraha; Siti Khomsah; Rima Dias Ramadhani; Tri Ginanjar Laksana
DEVOTE: Jurnal Pengabdian Masyarakat Global Vol. 1 No. 2 (2022): DEVOTE: Jurnal Pengabdian Masyarakat Global, Desember 2022
Publisher : LPPM Institut Pendidikan Nusantara Global

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (654.782 KB) | DOI: 10.55681/devote.v1i2.402

Abstract

Agribusiness utilizes inorganic waste that can be recycled to produce selling points such as basket bags from plastic waste and dolls from cloth waste. There is a superior product in this business unit, namely processed Jenitri seeds which are processed into handicrafts. Jenitri seeds are not only used as an ornamental tool. In certain beliefs, the work of Jenitri seeds is used as a worship tool and a medical device. Therefore, Jenitri seeds have a fairly high selling value compared to other handicrafts because of their various functions. Adiluhur Tourism Village is a village that is currently under development as a tourist spot with the name Kebumen English Tourism Village (KWIK) and is located in Adiluhur Village, Kec. Adimulyo, Kab. Kebumen. There are 3 business units that are superior and are still in the development stage, namely business units in the fields of tourism (agro-tourism), agriculture (agriculture), and handicrafts (agribusiness). Currently, the primary superior unit in Adiluhur Tourism Village is a business unit in the tourism sector. Agrotourism is managed by CV in collaboration with BUMDes (Village Owned Enterprise) Mulia Jaya. The tour featured in this unit is an introduction to several types of captive animals (various types of snakes, monitor lizards, sea urchins, iguanas, mongooses, Australian geckos, crocodiles, alligator fish, and many more) as well as a museum containing ancient agricultural tools (bronze spoon, harrows, sickles, hoes, nails, antique lamps, and many more). The potential that is being developed in this unit is outbound with the target visitor being Elementary Schools. Not only that, the agro-tourism manager plans to work with the BKSDA (Natural Resources Conservation Center) in caring for the animals in the unit.
Co-Authors Afandi Nur Aziz Thohari Amalia Beladinna Arifa Apri Junaidi Condro Kartiko Dian Nurdiansyah Ferani, Nia Annisa Nila Aulia Musa Nugraha, Novanda Alim Setya Ramadhani, Rima Dias Rindu Hafil Muhammadi Sheren Afryan Tiastama Sintiya Siti Khomsah