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All Journal Semantik Jurnal Pseudocode Wahana Teknik Sipil: Jurnal Pengembangan Teknik Sipil BANGUN REKAPRIMA Jurnal Teknologi Informasi Cyberku Jurnal Hilirisasi Technology kepada Masyarakat (SITECHMAS) Journal of Computing Theories and Applications Journal of Future Artificial Intelligence and Technologies
Stefanus Santosa
Politeknik Negeri Semarang
Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Social Sciences Transportation Other

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PEMODELAN PREDIKSI KUAT TEKAN BETON UMUR MUDA MENGGUNAKAN H2O'S DEEP LEARNING Santosa, Stefanus; Suroso, Suroso; Utomo, Marchus Budi; Martono, Martono; Mawardi, Mawardi
Wahana Teknik Sipil: Jurnal Pengembangan Teknik Sipil Vol 25, No 1 (2020): Wahana Teknik Sipil
Publisher : Politeknik Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32497/wahanats.v25i1.1917

Abstract

Artificial Neural Network (ANN) is a Machine Learning (ML) algorithm which learn by itself and organize its thinking to solve problems. Although the learning process involves many hidden layers (Deep Learning) this algorithm still has weaknesses when faced with high noise data. Concrete mixture design data has a high enough noise caused by many unidentified / measurable aspects such as planning, design, manufacture of test specimens, maintenance, testing, diversity of physical and chemical properties, mixed formulas, mixed design errors, environmental conditions, and testing process. Information needs about the compressive strength of early age concrete (under 28 days) are often needed while the construction process is still ongoing. ANN has been tried to predict the compressive strength of concrete, but the results are less than optimal. This study aims to improve the ANN prediction model using an H2O’s Deep Learning based on a multi-layer feedforward artificial neural network that is trained with stochastic gradient descent using backpropagation. The H2O’s Deep Learning best model is achieved by 2 hidden layers- 50 hidden neurons and ReLU activation function with a RMSE value of 6,801. This Machine Learning model can be used as an alternative/ substitute for conventional mix designs, which are environmentally friendly, economical, and accurate. Future work with regard to the concrete industry, this model can be applied to create an intelligent Batching and Mixing Plants.
PEMODELAN PREDIKSI KUAT TEKAN BETON UMUR MUDA MENGGUNAKAN H2O'S DEEP LEARNING Santosa, Stefanus; Suroso, Suroso; Utomo, Marchus Budi; Martono, Martono; Mawardi, Mawardi
Wahana Teknik Sipil: Jurnal Pengembangan Teknik Sipil Vol 25, No 1 (2020): Wahana Teknik Sipil
Publisher : Politeknik Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32497/wahanats.v25i1.1917

Abstract

Artificial Neural Network (ANN) is a Machine Learning (ML) algorithm which learn by itself and organize its thinking to solve problems. Although the learning process involves many hidden layers (Deep Learning) this algorithm still has weaknesses when faced with high noise data. Concrete mixture design data has a high enough noise caused by many unidentified / measurable aspects such as planning, design, manufacture of test specimens, maintenance, testing, diversity of physical and chemical properties, mixed formulas, mixed design errors, environmental conditions, and testing process. Information needs about the compressive strength of early age concrete (under 28 days) are often needed while the construction process is still ongoing. ANN has been tried to predict the compressive strength of concrete, but the results are less than optimal. This study aims to improve the ANN prediction model using an H2O’s Deep Learning based on a multi-layer feedforward artificial neural network that is trained with stochastic gradient descent using backpropagation. The H2O’s Deep Learning best model is achieved by 2 hidden layers- 50 hidden neurons and ReLU activation function with a RMSE value of 6,801. This Machine Learning model can be used as an alternative/ substitute for conventional mix designs, which are environmentally friendly, economical, and accurate. Future work with regard to the concrete industry, this model can be applied to create an intelligent Batching and Mixing Plants.
EVALUATION OF CLEAN WATER SUPPLY SYSTEMS OF DARUL HIKMAH MOSQUE IN POLITEKNIK NEGERI SEMARANG Santosa, Stefanus; Suhartono, Edy; Fahdiyat, Lukman; Pertiwi, Zulaikha Putri
Wahana Teknik Sipil: Jurnal Pengembangan Teknik Sipil Vol 25, No 1 (2020): Wahana Teknik Sipil
Publisher : Politeknik Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32497/wahanats.v25i1.1915

Abstract

Darul Hikmah Mosque is a building located inside Politeknik Negeri Semarang as a place of worship for all campus residents, and people who live around the area. The prayer activity is preceded by the Wudu. In order for Wudu to be carried out perfectly, sufficient quantities of water are needed. The lack of pressure and discharge of Wudu tap water, when used simultaneously, becomes a constraint that is the water pressure decreases. The purpose of this study is to determine the factors causing the lack of water discharge or pressure as required in SNI 03-7065-2005. The research method is quantitative descriptive by evaluating the performance of the existing water supply system and using a discrepancy model by comparing the evaluation results with existing data to find discrepancies. Evaluation results indicate that the factors that cause the lack of water pressure are the roof tank height is less than ideal for water distribution with gravitational power, and the volume of available water is not sufficient
EVALUATION OF CLEAN WATER SUPPLY SYSTEMS OF DARUL HIKMAH MOSQUE IN POLITEKNIK NEGERI SEMARANG Santosa, Stefanus; Suhartono, Edy; Fahdiyat, Lukman; Pertiwi, Zulaikha Putri
Wahana Teknik Sipil: Jurnal Pengembangan Teknik Sipil Vol 25, No 1 (2020): Wahana Teknik Sipil
Publisher : Politeknik Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32497/wahanats.v25i1.1915

Abstract

Darul Hikmah Mosque is a building located inside Politeknik Negeri Semarang as a place of worship for all campus residents, and people who live around the area. The prayer activity is preceded by the Wudu. In order for Wudu to be carried out perfectly, sufficient quantities of water are needed. The lack of pressure and discharge of Wudu tap water, when used simultaneously, becomes a constraint that is the water pressure decreases. The purpose of this study is to determine the factors causing the lack of water discharge or pressure as required in SNI 03-7065-2005. The research method is quantitative descriptive by evaluating the performance of the existing water supply system and using a discrepancy model by comparing the evaluation results with existing data to find discrepancies. Evaluation results indicate that the factors that cause the lack of water pressure are the roof tank height is less than ideal for water distribution with gravitational power, and the volume of available water is not sufficient
Prediksi Produksi Air PDAM dengan Jaringan Syaraf Tiruan Yudha Tirto Pramonoaji; Stefanus Santosa; Ricardus Anggi Pramunendar
Semantik Vol 3, No 1 (2013): Semantik 2013
Publisher : Semantik

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (436.826 KB)

Abstract

Perusahaan Daerah Air Minum  (PDAM)  merupakan perusahaan milik daerah yang begerak di bidang  penyedia, pengolahan,dan pendistribusian air bersih.  Sebuah sistem  yang  akurat untuk  prediksi  jumlah produksi air  untuk masa depan  dibutuhkan oleh PDAM untuk menentukan kebijakan dalam bidang produksi air. Penelitian ini menghasilkan sebuah model prediksi untuktotal volume  produksi air PDAM  Kota Semarang.  Data yang diolah  adalah jumlah penduduk, jumlah pelanggan berdasarkan jenis pelanggan, total volume produksi, kontribusi daerah sumber, volume distribusi, air terjual, dan kehilangan air. Data diperoleh dari laporan  bulanan  perusahaan  selama  5  tahun terakhir yaitu  mulai tahun 2008-2012.  Pendekatan  yang digunakan untuk prediksi produksi air adalah dengan menggunakan metode  Jaringan Syaraf Tiruan Backpropagation dengan Fungsi Aktivasi Hyperbolic Tangent. Berdasarkan hasil  penelitian, diperoleh  sebuah Model Prediksi Untuk  Total Volume Produksi Air PDAM  Kota Semarang dengan  nilai error 0.000000, MSE 0.074416,  MAE 0.102487, dan  rata-rata nilai akurasi sebesar 95,56%.
Penentuan Prioritas Perbaikan Gedung Menggunakan Metode Ahp Dari Sudut Pandang Tenant Stefanus Santosa; Mochammad Tri Rochadi; Suroso Suroso; Mawardi Mawardi; Suwarto Suwarto
Wahana Teknik Sipil: Jurnal Pengembangan Teknik Sipil Vol 24, No 2 (2019): Wahana Teknik Sipil
Publisher : Politeknik Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32497/wahanats.v24i2.1727

Abstract

Bangunan mall adalah pusat perbelanjaan yang secara arsitektur berupa bangunan tertutup dengan suhu yang diatur dan memiliki jalur yang teratur. Infrastruktur bangunan mall terdiri dari beberapa tenant yang menawarkan berbagai macam kebutuhan masyarakat serta adanya atrium dalam mall yang berfungsi sebagai tempat pameran. Tujuan dari penelitian ini adalah mengetahui urutan prioritas dan kondisi bangunan mall, yang berguna sebagai acuan dalam kegiatan perbaikan yang berimbas pada kepuasan tenant. Permasalahan kepuasan tenant dapat diakibatkan dari berbagai faktor salah satunya, yaitu terkait fasilitas, perbaikan gedung dan penanganannya yang dikhawatirkan dapat mempengaruhi produktivitas. Data primer pada penelitian ini diperoleh dengan cara pengamatan langsung di lokasi penelitian, dan menyebarkan form kuisioner kepada responden yang terkait. Data sekunder diperoleh dari data penelitian terdahulu, peraturan yang berlaku, dan pedoman bangunan gedung. Penelitian ini menggunakan metode Analytical Hierarchy Process (AHP), untuk menentukan bobot komponen yang diperoleh dari hasil penilaian kepentingan komponen bangunan, oleh masing-masing responden. Hasil analisis menunjukkan bahwa faktor pertimbangan utama perbaikan gedung peringkat pertama adalah faktor kenyamanan dengan nilai prioritas 21,299%, peringkat kedua faktor keselamatan dengan nilai prioritas 18,343%, peringkat ketiga faktor dana/ biaya yang terbatas dengan nilai prioritas 16,192%. Berdasarkan model hierarki yang telah disusun, maka dapat ditemukan Model Prioritas Pengambilan Keputusan Perbaikan Gedung khususnya Mall dengan mempertimbangkan faktor kenyamanan, kesehatan, keselamatan, kemudahan akses, dana/ biaya yang terbatas, petugas yang terbatas, dan waktu yang terbatas (deadline).
MODEL PREDIKSI SLUMP BETON DENGAN ARTIFICIAL NEURAL NETWORKS- BACKPROPAGATION STEFANUS SANTOSA Dr. Drs, M.Kom.; BASUKI SETIYO BUDI S.T., M.T.; JUNAIDI S.T., M.Eng.; TJOKRO HADI SST., M.T.
Wahana Teknik Sipil: Jurnal Pengembangan Teknik Sipil Vol 21, No 02 (2016): WAHANA Teknik Sipil
Publisher : Politeknik Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32497/wahanats.v21i02.835

Abstract

The design value of slump is often done manually by calculating the value of cement water factor in order to obtain the desired slump value. But these designs often unreliable. This study proposes a model prediction of concrete slump design for a variety of quality concrete with variables that are more complex than other studies. From a series of experiments with various models using Artificial Neural Network- Backpropagation (BPNN), the smallest RMSE values obtained models that can be achieved is by 0.004294661. Best Setting model parameters are Training Cycles: = 100,000, Learning Rate = 0.001, Momentum: = 0.2, Hidden Layer Size: = 10, and Number of Hidden layer: = 1.Kata kunci : prediction, concrete slump, artificial neural network, backpropagation.
Seleksi Arah Sudut Komputasi Dan Fitur Glcm Pada Kstraksi Citra Kayu Jati, Mahoni, Mindi, Dan Sengon Stefanus Santosa; Martono Martono; Marchus Budi Utomo; Basuki Setiyo Budi
Wahana Teknik Sipil: Jurnal Pengembangan Teknik Sipil Vol 23, No 2 (2018): Wahana Teknik Sipil
Publisher : Politeknik Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32497/wahanats.v23i2.1363

Abstract

Research on feature extraction of wood texture with features and angle direction israrely done, especially in teak, mahogany, mindi, and albasia. This research isneeded to select more efficient and effective features and angle directions to identify wood species. The features tested were Angular Second Moment (ASM), Contrast, IDM / Homogenity, Entropy, Correlation and the direction of the computational 0, 45, 90, and 135 degrees of gray level co-occurrence matrix (GLCM). The experimental results show that the selected angles are 0, 45, and 90 degrees and features are IDM and Entropy.Kata kunci : gray level co-occurrence matrix (GLCM), features extraction, woodclassification
ALGORITMA KLASIFIKASI DATA MINING NAIVE BAYES BERBASIS PARTICLE SWARM OPTIMIZATION UNTUK DETEKSI PENYAKIT JANTUNG Nur Aeni Widiastuti; Stefanus Santosa; Catur Supriyanto
Jurnal Pseudocode Vol 1, No 1 (2014)
Publisher : Universitas Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (148.155 KB) | DOI: 10.33369/pseudocode.1.1.11-14

Abstract

Data mining sering disebut Knowledge Discovery in Database (KDD). Data mining biasanya digunakan untuk memperbaiki pengambilan keputusan di masa yang akan datang berdasarkan informasi yang diperoleh dari masa lalu. Misalnya untuk prediksi, estimasi, assosiasi, clustering, dan deskripsi. Sekumpulan data yang ada di laboratorium klinik belum difungsikan secara efektif dan hanya di fungsikan sebagai arsip untuk riwayat penyakit pasien. Jantung merupakan pembunuh nomor satu di dunia. Kurangnya aliran darah dan oksigen ke jantung bisa menyebabkan penyakit jantung. Pada penelitian ini akan membandingkan algoritma klasifikasi data mining Naive Bayes Berbasis PSO untuk deteksi penyakit jantung. Pengukuran dengan Naives Bayes menghasilkan akurasi 82.14%, sementara dengan Naives Bayes Berbasis Particle Swarm Optimization akurasi meningkat menjadi 92.86%. Tingkat akurasi dibandingkan dengan hasil laboratorium.Kata Kunci: Data Mining, Penyakit Jantung, Naive Bayes, Particle Swarm Optimization. 
SISTEM INFORMASI PERENCANAAN PENGADAAN OBAT DI DINAS KESEHATAN KABUPATEN BOYOLALI Erni Rahmawatie; Stefanus Santosa
Jurnal Pseudocode Vol 2, No 1 (2015)
Publisher : Universitas Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (239.227 KB) | DOI: 10.33369/pseudocode.2.1.45-52

Abstract

Obat memegang peran yang penting dalam pelayanan kesehatan karena obat merupakan salah satu sarana untuk meningkatkan derajat kesehatan. Dinas Kesehatan Kabupaten Boyolali Propinsi Jawa Tengah merupakan dinas yang salah satu tugasnya adalah merencanakan, melaksanakan, mengarahkan, mengawasi, dan mengendalikan bidang kesehatan sesuai kebijakan pemerintah daerah. Setiap bulannya salah satu program Dinas Kesehatan Kabupaten Boyolali adalah memantau kegiatan setiap puskesmas berkaitan dengan 10 penyakit terbanyak, trend penyakit, stok obat, dan penggunaan obat. Data tersebut menjadi acuan untuk membantu pemerintah daerah dalam mendukung keputusan pemerintah dalam perencanaan pengadaan obat. Penelitian ini bertujuan untuk membuat sistem yang mampu menghasilkan informasi yang dapat mendukung perencanaan pengadaan obat oleh pemerintah daerah. Metode pengembangan sistem yang digunakan dalam penelitian ini adalah SDLC (System Development Live Cycle).Kata kunci: Sistem Informasi, Perencanaan, Pengadaan Obat
Co-Authors Abd. Rasyid Syamsuri Adityawan, Harish Trio Agus Setyawan Agus Widjanarko Ahmad Zainul Fanani Ajib Susanto Ali Sofyan Anung Suwarno, Anung April Firman Daru Basuki Setiyo Budi BASUKI SETIYO BUDI S.T., M.T. Catur Supriyanto Catur Supriyanto Catur Supriyanto Supriyanto De Rosal Ignatius Moses Setiadi Dewi Nurdiyah Dianita Ratna Kusumastuti Edi Noersasongko Erni Rahmawatie Fahdiyat, Lukman Fahdiyat, Lukman Farroq, Omar Fatkhuroji Fatkhuroji Fenilinas Adi Artanto Gan, Hong-Seng Goro, Garup Lambang Hadi Wibowo Hadi, Tjokro Hario Guritno Heri Triluqman Budisantoso Ilala, Oze Dora Indah Munitasri Islam, Hussain Md Mehedul Isnubroto, Danang Jadi . Joko, Karnawan JUNAIDI S.T., M.Eng. Karnawan Joko Setiyono Khairul Fahmi Leily Fatmawati, Leily M. Arief Soeleman Marchus Budi Utomo Marchus Budi Utomo, Marchus Budi Marsudi Marsudi Martono Martono Martono Martono Martono Martono Mawardi Mawardi Mochammad Tri Rochadi Nur Aeni Widiastuti Ojugo, Arnold Adimabua Pertiwi, Zulaikha Putri Pertiwi, Zulaikha Putri Praharseno, Fikri Pratama, M Hafidh Aditya Putra, Erwin Dwika Rabinah, Aiun Hayatu Ricardus Anggi Pramunendar Rifqi Aulia Abdillah, Rifqi Aulia Roselina Rahmawati Roy Yuliantara S, Sri Wahyuningsih Sarker, Md Kamruzzaman Setiyono, Karnawan Joko Setyaningsih, Desi SUDARMONO SUDARMONO Suhartono, Edy Sukoyo Sukoyo Sulaiman, Sri Wahyuningsih Sulaiman, Sriwahyuningsih Supriyadi Supriyadi Supriyo Supriyo Suroso Suroso Suroso Suroso Suwarto Suwarto Suwarto Suwarto Tjokro Hadi TJOKRO HADI SST., M.T. Triatmo Sugih Hardono W, Herry Ludiro Wahyono, Herry Ludiro Wicaksono, M Rafi Wiji Lestari Yonathan Purbo Santosa Yudha Tirto Pramonoaji Yusetyowati Yusetyowati, Yusetyowati Zenal Arifin Zuama, Leygian Reyhan