Judul: Optimalisasi produksi tempe pada Rumah Tempe Indonesia (RTI), Cilendek, Bogor
Abstrak: Rumah Tempe Indonesia (RTI) merupakan industri tempe baru yang modern di Bogor. Sebagai sebuah unit usaha RTI memiliki tujuan untuk memaksimumkan keuntungan dan mengalokasikan sumber daya sebaik mungkin. Sumber daya yang terbatas dan keputusan produksi yang bergantung pada job order menjadi kendala yang harus dihadapi. Kondisi produksi RTI dirumuskan dalam model linear yang dikenal dengan linear programming (LP). Model tersebut diolah untuk mencari kombinasi produksi yang menggambarkan kondisi produksi aktual. Berdasarkan hasil analisis diketahui bahwa kombinasi dan tingkat produksi tempe di RTI belum mencapai kondisi optimal karena masih terdapat sumber daya yang berlebih ketersediaannya, yaitu: kedelai, ragi, kemasan, jam kerja TKL, jam kerja mesin pemecah kedelai, jam kerja mesin mixer kedelai, dan jam kerja mesin sealer. Job order menjadi kendala bagi RTI dalam mencapai tingkat produksi yang optimal. Akibat adanya job order, RTI masih berproduksi di bawah kapasitas produksinya padahal RTI memiliki akses terhadap sumber daya yang lebih banyak lagi. RTI dapat meningkatkan produksi sesuai dengan kapasitasnya melalui peningkatan penggunaan dan ketersediaan kedelai serta sumber daya lainnya. Dengan demikian, RTI dapat meningkatkan keuntungannya hingga 105.90 persen dari keuntungan awal pada kondisi aktual. Peningkatan produksi harus diiringi dengan peningkatan promosi sehingga tidak ada produk yang terbuang. Peningkatan promosi dan penjualan terhadap brand RTI sendiri, tempe “Kita”, akan mengurangi ketergantungan produksi terhadap job order, bahkan memberikan keuntungan yang lebih besar lagi bagi RTI.
Keyword: Job Order, Optimization, Linier Programming, Tempe

Judul: Redesign of the Layout of Tempe Production Process Facilities at Rumah Tempe Indonesia (RTI)
Abstrak: Based on data from the Ministry of Agriculture, the level of tempe consumption in Indonesia will continue to increase until 2021. The increase in tempe consumption encourages the tempe industry to be able to keep up with the times and improve the quality of its industry in Indonesia. One solution is to fix the system in the production facility layout. Rumah Tempe Indonesia (RTI) is an SME in the city of Bogor which is engaged in processing soybeans into tempe products in plastic packaging. Layout problems occur at milling stations that are far from the filtering station, resulting in backtracking and some facilities are too close together so that they become narrow. The layout redesign was carried out using the SLP (Systematic Layout Planning) method and alghrithm CORELAP (Computerized Layout Planning), which is a constructive algorithm that can produce the best layout. Furthermore, the layout is simulated using the software Flexsim. The simulation results of the proposed layout using the CORELAP algorithm method resulted in an increase in the efficiency of the material moving distance by 8.54% and the displacement time by 42.67%.
Keyword: Layout, Workstation, CORELAP, Flexsim

Judul: Metagenome Classification Using Naïve Bayes Classifier Method.
Abstrak: Metagenome study is an important step in the taxonomic grouping. Grouping can be conducted using binning method. Binning is required to determine contigs of each phylogenetic species groups. In this study, binning is done using supervised learning based composition approach. We used NaïveBayes Classifier method for performing supervised learning and employed counting of k-mer frequencies for extracting feature. The classification process was conducted at genus-level taxon. The results showed that using short fragments (400 bp), our method could obtain the accuracy of 49.34 % and 53.95 % with features of 3-mers dan 4-mers frequencies, respectively. Meanwhile, the accuracy of our method was significally increased when classifying long fragments (10 kbp). Our method could obtain the accuracy of 82.23% with 3-mers frequencies feature and 85.89% with 4-mers frequencies feature. It can be concluded that the accuracy of our classifier was increased by increasing the size of fragments. Moreover, in this research, the 4- mers frequencies feature gave the best results for classifying metagenome fragments.
Keyword: metagenome, k-mers, Naïve Bayes Classifier, binning